Covid19Japan.com

Covid19 Japanが独自に収集している陽性者単位のデータ（個票データ）。ソースとデータは全てGitHubにて公開されており、データはJSON形式。「レコード数 \(\neq\) 累計陽性者数」であることに注意。

　

Import

Covid19 JapanがGitHubで公開しているデータは前述のようにJSON形式であり、最新データはlatest.jsonファイルで示されている。このため、読み込む際はひと工夫必要。

個票データ（Patient Data）

陽性者単位の個票データ。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/patient_data/"

df <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df

　

集計データ（Summary Data）

死亡者数や重症者数などの推移データはsummaryフォルダ内のJSON形式ファイルにまとめられている。読み込むと分かるがリスト型で、その中データフレームが含まれる形式である。
summaryフォルダの他にsummary_minフォルダというフォルダがあるが、summary_minフォルダ内のJSONファイルは単に改行を省略して小さくしたファイル。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/summary/"

df_s <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df_s %>% summary()

##             Length Class      Mode     
## prefectures 27     data.frame list     
## regions     12     data.frame list     
## daily       37     data.frame list     
## updated      1     -none-     character

　
三つのデータフレームと一つのベクトル（更新日時）から構成されている。データフレームは上から順に都道府県別、地方別、日次となっているが、Lengthを見てわかるようにそれぞれに含まれる集計データが異なっている。

　

都道府県単位集計

更新日時（$updated）における都道府県単位での累積値。厚生労働省がオープンデータから除いている空港検疫・ダイヤモンドプリンセス・長崎クルーズ船・その他が含まれるので全51区分になっている。

df_s$prefectures

陽性者・死亡者などの時系列集計データがネストされて格納されている。日付はネストされていないので、各項目に対するstartDateの項を参照すること。

項目	内容	備考
dailyConfirmedCount	陽性者数	単日
dailyConfirmedStartDate	陽性者数のカウント開始日	区分により開始日が異なる
dailyDeceasedCount	死亡者数	単日
dailyDeceasedStartDate	死亡者数のカウント開始日	区分により開始日が異なる
dailyRecoveredCumulative	快復者数	累計
dailyRecoveredStartDate	快復者数のカウント開始日	区分により開始日が異なる
dailyActive	治療者数1	単日
dailyActiveStartDate	治療者数のカウント開始日	区分により開始日が異なる

¹ 陽性者数から死亡者数と快復者数を引いた数値を治療者数としている

　

地方単位集計

更新日次時点における地方区分単位での累積値。陽性者の時系列集計データが都道府県単位データと同様にネストで格納されているが、死亡者・快復者・治療者のデータは含まれていない。
なお、時系列データの合計値と累積項の値が一致しない場合がある。

df_s$regions

df_s$regions$dailyConfirmedCount[[1]] %>% sum()

## [1] 61201

　

日次集計

個票データを日次で集計したもの。日付を見れば分かる通り暗黙の欠落を含んでいる。

df_s$daily

　

更新日時

集計データの更新日時。

df_s$updated

## [1] "2020-11-08T20:51:52+09:00"

　

Area Data

地域・地方ごとの分析を行う場合に便利な都道府県データを用意した。このデータはGistで公開している。

　

Others

その他のオープンデータ。

• 新型コロナウイルス対策病床オープンデータ

　

Data Wrangling

Summarize

最初に個票データの内容を確認する。これには要約に便利なskimrパッケージを用いる。

df %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	110081
Number of columns	23
_______________________
Column type frequency:
character	19
logical	3
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	1	8	0	108387	0
dateAnnounced	0	1.00	10	10	0	285	0
gender	17928	0.84	1	1	0	2	0
detectedPrefecture	0	1.00	3	15	0	49	0
patientStatus	106091	0.04	8	23	0	8	0
notes	57162	0.48	1	270	0	50150	1
mhlwPatientNumber	109632	0.00	1	11	0	434	0
prefecturePatientNumber	15609	0.86	5	20	0	94463	0
prefectureSourceURL	78777	0.28	5	224	0	3440	0
residence	25903	0.76	1	38	0	1423	0
sourceURL	637	0.99	1	239	0	8173	0
relatedPatients	99463	0.10	2	259	0	6442	0
knownCluster	107598	0.02	3	88	0	229	0
detectedCityTown	83787	0.24	2	22	0	663	0
cityPrefectureNumber	84055	0.24	1	34	0	26017	2
citySourceURL	98214	0.11	9	317	0	3646	0
deceasedDate	108250	0.02	10	10	0	235	0
deceasedReportedDate	108866	0.01	10	62	0	205	0
deathSourceURL	109011	0.01	14	123	0	651	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	0.98	TRU: 108386, FAL: 1695
charterFlightPassenger	110067	0	1.00	TRU: 14
cruisePassengerDisembarked	110070	0	1.00	TRU: 11

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
ageBracket	0	1	31.62	23.9	-1	20	30	50	100	▅▇▅▂▁

　
元がJSON形式なので、読み込んだ直後は殆どの変量（フィーチャー）が文字型になっていることが分かる。また、意外と欠損が多いことも分かる。

　

Tidy & Transform

各変量（フィーチャー）を適切な形式に変換し、地域区分でも分析できるように都道府県データと結合することで、ベースとなるデータセットを作成する。

x <- df %>% 
  dplyr::select(patientId, date = dateAnnounced, gender,
                pref = detectedPrefecture, patientStatus, knownCluster,
                confirmedPatient, charterFlightPassenger,
                cruisePassengerDisembarked, ageBracket,
                deceasedDate, deceasedReportedDate) %>% 
  dplyr::filter(confirmedPatient == TRUE) %>% 
  dplyr::mutate(date = lubridate::as_date(date),
                gender = forcats::as_factor(gender),
                patientStatus = forcats::as_factor(patientStatus),
                cluster = dplyr::if_else(!is.na(knownCluster), TRUE, FALSE),
                ageBracket = forcats::as_factor(ageBracket),
                deceasedDate = lubridate::as_date(deceasedDate),
                deceasedReportedDate = lubridate::as_date(deceasedReportedDate)) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::select(-`推計人口`) %>% 
  dplyr::rename(Pref = `都道府県`, region = `八地方区分`)

x

変換結果を要約してみると

x %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	108386
Number of columns	19
_______________________
Column type frequency:
character	3
Date	3
factor	9
logical	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	1	8	0	108386	0
pref	0	1.00	3	15	0	49	0
knownCluster	105932	0.02	3	88	0	227	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
date	0	1	2020-01-15	2020-11-08	2020-08-15	285
deceasedDate	108007	0	2020-02-13	2020-10-17	2020-05-08	150
deceasedReportedDate	108056	0	2020-02-13	2020-10-17	2020-05-16	131

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
gender	17256	0.84	FALSE	2	M: 51178, F: 39952
patientStatus	105853	0.02	FALSE	8	Hos: 1261, Dec: 371, Hom: 315, Dis: 283
ageBracket	0	1.00	FALSE	13	20: 24994, -1: 17353, 30: 15824, 40: 13121
pcode	1278	0.99	FALSE	47	13: 32648, 27: 13816, 14: 9354, 23: 6819
Pref	1278	0.99	FALSE	47	東京都: 32648, 大阪府: 13816, 神奈川: 9354, 愛知県: 6819
region	1278	0.99	FALSE	8	関東地: 55846, 近畿地: 21721, 九州地: 11232, 中部地: 10606
広域圏	8809	0.92	FALSE	8	首都圏: 56079, 近畿圏: 21126, 中部圏: 9268, 九州圏: 7711
通俗的区分	1278	0.99	FALSE	11	関東: 55846, 関西: 21126, 東海: 8899, 九州: 7711
fct_pref	1278	0.99	FALSE	47	Tok: 32648, Osa: 13816, Kan: 9354, Aic: 6819

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	1.00	TRU: 108386
charterFlightPassenger	108372	0	1.00	TRU: 14
cruisePassengerDisembarked	108375	0	1.00	TRU: 11
cluster	0	1	0.02	FAL: 105932, TRU: 2454

　
文字型を因子型に変換するだけでも大まかな傾向が見えるようになる。例えば

• 年齢別で見ると20代、30代、年齢不明（恐らく非回答）、40代の順に多い
• 都道府県別では東京、大阪、神奈川、愛知の順と人口にほぼ比例
• 地方区分で見ると関東、近畿、九州、中部となっており九州地方が以外と多い

ことが読める。

patientStatusは以下の通りで、ほぼ更新されていないのと思われる。死者数などの推移を見る場合はサマリデータを使った方がいい。

x %>% 
  dplyr::group_by(patientStatus) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(Japanese = c("回復", "入院中", "退院済", "死亡", "詳細不明",
                             "重症", "自宅療養", "ホテル療養", NA))

　

集計（陽性者）

　

地方別集計

地方別で陽性者数と人口千人あたりの比率を比較する。

region <- prefs %>% 
  dplyr::group_by(`八地方区分`) %>% 
  dplyr::summarise(population = sum(`推計人口`)) %>% 
  dplyr::rename(region = `八地方区分`)

r_by_region <- x %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(region, by = c("region" = "region")) %>% 
  dplyr::select(region, n, population) %>% 
  dplyr::mutate(rate = round(n / population, 2))
r_by_region

　

都道府県別集計

同様に都道府県別に比較する。

r_by_prefs <- x %>% 
  dplyr::group_by(Pref) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(prefs, by = c("Pref" = "都道府県")) %>% 
  dplyr::select(Pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2))
r_by_prefs %>% 
  tibble::rowid_to_column("No") %>% 
  DT::datatable()

　

累計陽性者数トップ10

x %>% 
  dplyr::group_by(Pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::left_join(prefs, by = c("Pref" = "都道府県")) %>% 
  dplyr::select(Pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2)) %>% 
  dplyr::slice_max(order_by = n, n = 10) %>% 
  dplyr::rename(`累計陽性者数` = n, `推計人口[千人]` = population, `率` = rate)

　

人口千人あたりの比率トップ10

x %>% 
  dplyr::group_by(Pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::left_join(prefs, by = c("Pref" = "都道府県")) %>% 
  dplyr::select(Pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2)) %>% 
  dplyr::slice_max(order_by = rate, n = 10) %>% 
  dplyr::rename(`累計陽性者数` = n, `推計人口[千人]` = population, `率` = rate)

　

クラスタ比率

x %>% 
  dplyr::group_by(region, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1))

　

x %>% 
  dplyr::group_by(Pref, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  tidyr::replace_na(list(`TRUE` = 0L, ratio = 0.0)) %>% 
  tibble::rowid_to_column(var = "No") %>% 
  DT::datatable()

　

日次集計（陽性者）

　

全国日次集計

日次単位の陽性者数、前日差、累計、移動平均（7日）を求める。

x_by_all <- x %>% 
  dplyr::group_by(date) %>% 
  dplyr::filter(!is.na(Pref)) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day"),
                  fill = list(n = 0L)) %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n),
                ma7 = zoo::rollmeanr(n, k = 7L, na.pad = TRUE))

x_by_all

　

地方区分別日次集計

同様に地方区分別の集計を求める。

x_by_region <- x %>% 
  dplyr::filter(!is.na(Pref)) %>% 
  dplyr::group_by(date, region) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = region, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "region", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs %>% dplyr::distinct(`八地方区分`), .,
                   by = c("八地方区分" = "region")) %>% 
  dplyr::mutate(region = forcats::fct_inorder(`八地方区分`)) %>% 
  dplyr::select(date, region, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
x_by_region

　

都道府県別日次集計

同様に都道府県別の集計を求める。

x_by_prefs <- x %>% 
  dplyr::filter(!is.na(Pref)) %>% 
  dplyr::group_by(date, Pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = Pref, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "Pref", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(Pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "Pref")) %>% 
  dplyr::mutate(Pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::select(date, Pref, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
x_by_prefs

　

日次集計（死亡者）

　

都道府県別

都道府県別の日次単位の死亡者数、前日差、累計、移動平均（7日）を求める。

start <- df_s$prefectures %>% 
  dplyr::select(pref = name, date = dailyDeceasedStartDate) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::arrange(pcode) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(date, Pref = `都道府県`) %>% 
  dplyr::distinct(date) %>% 
  .$date %>% lubridate::as_date()

d_by_prefs <- df_s$prefectures %>% 
  dplyr::select(deceased = dailyDeceasedCount, pref = name) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(Pref = `都道府県`, deceased) %>% 
  tidyr::unnest(deceased) %>% 
  tidyr::pivot_wider(names_from = Pref, values_from = deceased) %>% 
  tidyr::unnest() %>% 
  dplyr::mutate(date = seq.Date(from = start, to = start + nrow(.) - 1,
                                by = "day")) %>% 
  dplyr::select(date, dplyr::everything()) %>% 
  tidyr::pivot_longer(col = -date, names_to = "Pref", values_to = "n") %>% 
  dplyr::group_by(Pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "Pref")) %>% 
  dplyr::mutate(Pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::select(date, Pref, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
d_by_prefs

　

地域別

集計データ$regionsには死亡者数の日次データが存在しないため$prefecturesのデータから計算する。

d_by_region <- d_by_prefs %>% 
  dplyr::select(date, pref = Pref, n) %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::group_by(date, `八地方区分`) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::rename(region = `八地方区分`) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::arrange(date)
d_by_region

　

全国日次集計

都道府県別のデータから全国の日次集計を求める。

d_by_all <- d_by_prefs %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n))
d_by_all

　

Visualize

　

陽性者の日次推移

　

全国

sec_scale <- 100
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("全国の陽性者数推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("全国の陽性者数前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別

陽性者数（単日）

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = n)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      width = 1.0, alpha = 0.5) + 
    ggplot2::labs(title = paste0("地方別陽性者数推移（単日） @", datetime),
                  caption = caption, 
                  x = "", y = "陽性者数") 

　

移動平均

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = ma7, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') +
    ggplot2::labs(title = paste0("地方別7日間移動平均 @", datetime),
                  caption = caption,
                  x = "", y = "陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 3) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

累積

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = cum, colour = region)) + 
    ggplot2::geom_line() +
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("地方別累積陽性者数 @", datetime),
                  caption = caption,
                  x = "", y = "累積陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)))

　

単日＋累積

sec_scale <- 50
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

前日差

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = region)) +
    ggplot2::facet_wrap(~ region, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  caption = caption, x = "", y = "")

　

都道府県別

　

単日＋累計

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


x_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = Pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = Pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = Pref)) +
    ggplot2::facet_wrap(~ Pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = Pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = Pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = Pref)) +
    ggplot2::facet_wrap(~ Pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

　

前日差

x_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = Pref)) +
    ggplot2::facet_wrap(~ Pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  x = "", y = "")

　

死亡者の日次推移

　

全国

sec_scale <- 100
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("全国の死亡者数推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("全国の死亡者数前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別

sec_scale <- 50
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

都道府県別日次推移

sec_scale <- 10
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = Pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = Pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = Pref)) +
    ggplot2::facet_wrap(~ Pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = Pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = Pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = Pref)) +
    ggplot2::facet_wrap(~ Pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

比較

陽性者数と死亡者の比較。

　

全国

sec_scale <- (1 / 10)

x_by_all %>% 
  dplyr::left_join(d_by_all, by = c("date")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

地方別

sec_scale <- (1 / 10)
ncol <- 5

x_by_region %>% 
  dplyr::left_join(d_by_region, by = c("date" = "date", "region" = "region")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

相関

　

地方区分別

r_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

都道府県別

r_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = Pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = Pref, colour = Pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

r_by_prefs %>% 
  dplyr::filter(n < 5000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = Pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = Pref, colour = Pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("累計陽性者数五千人未満 @", datetime),
                  caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

Model

時系列（TS）分析

日本の時系列データは週単位の変動が認められるので、frequencyを7に設定して陽性者数のデータをtsオブジェクトに変換する。

ts_week <- x_by_all %>% 
  dplyr::select(n) %>% 
  ts(frequency = 7)

時系列データに変換したものをプロットすると可視化の項でプロットした棒グラフと同じような形のグラフになることが分かります。

ts_week %>% 
  plot(main = paste0("全国 @", datetime))

上記からトレンド（長期的傾向）を除いたグラフ。デフォルト指定なのでlag = 1。つまり、前日差。

ts_week %>% 
    base::diff() %>% 
  plot(main = paste0("全国 @", datetime))

トレンド、季節変動（周期変動）、非周期変動に分解した場合。frequency = 1では分解できない点に注意。

ts_week %>% 
  stats::decompose() %>% 
  plot()

トレンドを抜き出してみる。移動平均に酷似している。

ts_week %>% 
  stats::decompose() %>% 
  .$x %>% 
  plot(ylim = c(0, 1500), main = paste0("全国 @", datetime))

par(new = TRUE)

ts_week %>% 
  stats::decompose() %>% 
  .$trend %>% 
  plot(ylim = c(0, 1500), col = "dark green", lwd = 3)

　

地方別時系列分析

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

oldpar <- par()
par(mfrow=c(4, 2))
x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name, ylim = c(0, max(.x)))
                # plot(.x, main = region)
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 3)
                  # plot(col = "dark green", lwd = 2)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

par(oldpar)

x_by_prefs %>% 
  dplyr::select(Pref, n) %>% 
  split(.$Pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name, ylim = c(0, max(.x)))
                # plot(.x, main = region)
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 3)
                  # plot(col = "dark green", lwd = 2)
              } )

## $北海道
## NULL
## 
## $青森県
## NULL
## 
## $岩手県
## NULL
## 
## $宮城県
## NULL
## 
## $秋田県
## NULL
## 
## $山形県
## NULL
## 
## $福島県
## NULL
## 
## $茨城県
## NULL
## 
## $栃木県
## NULL
## 
## $群馬県
## NULL
## 
## $埼玉県
## NULL
## 
## $千葉県
## NULL
## 
## $東京都
## NULL
## 
## $神奈川県
## NULL
## 
## $新潟県
## NULL
## 
## $富山県
## NULL
## 
## $石川県
## NULL
## 
## $福井県
## NULL
## 
## $山梨県
## NULL
## 
## $長野県
## NULL
## 
## $岐阜県
## NULL
## 
## $静岡県
## NULL
## 
## $愛知県
## NULL
## 
## $三重県
## NULL
## 
## $滋賀県
## NULL
## 
## $京都府
## NULL
## 
## $大阪府
## NULL
## 
## $兵庫県
## NULL
## 
## $奈良県
## NULL
## 
## $和歌山県
## NULL
## 
## $鳥取県
## NULL
## 
## $島根県
## NULL
## 
## $岡山県
## NULL
## 
## $広島県
## NULL
## 
## $山口県
## NULL
## 
## $徳島県
## NULL
## 
## $香川県
## NULL
## 
## $愛媛県
## NULL
## 
## $高知県
## NULL
## 
## $福岡県
## NULL
## 
## $佐賀県
## NULL
## 
## $長崎県
## NULL
## 
## $熊本県
## NULL
## 
## $大分県
## NULL
## 
## $宮崎県
## NULL
## 
## $鹿児島県
## NULL
## 
## $沖縄県
## NULL

　

Infer

時系列予測（ARIMA）

ARIMA（Auto Regressive Integrated Moving Average, 自己回帰和分移動平均）モデルによる陽性者に対する予測。予測に必要なパラメータはステップワイズにより自動的に最適なものが選択される。ただし、モデル自体を評価していないので、こういうことが出来る程度の話。

　

全国

x_by_all %>% 
  dplyr::select(n) %>% 
  ts(.$n, frequency = 7) %>% 
  forecast::auto.arima() %>%  
  forecast::forecast() %>% 
  plot(main = paste0("全国 @", datetime))

　

地方別

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## $北海道地方$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 184.2972 159.7512 173.5928 180.8045 184.5220 205.7002 197.6753 207.7486
##  [9] 201.6470 204.1179 210.4270 210.5461 221.8307 218.0035
## 
## $北海道地方$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 174.6240 169.5033
## 43.85714 148.9590 143.2459
## 44.00000 160.1047 152.9645
## 44.14286 166.1002 158.3161
## 44.28571 168.0754 159.3691
## 44.42857 188.0720 178.7403
## 44.57143 178.6949 168.6472
## 44.71429 186.4072 175.1097
## 44.85714 178.6890 166.5359
## 45.00000 179.3337 166.2137
## 45.14286 184.1450 170.2321
## 45.28571 182.7181 167.9868
## 45.42857 192.6217 177.1593
## 45.57143 187.4221 171.2333
## 
## $北海道地方$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 193.9704 199.0911
## 43.85714 170.5435 176.2566
## 44.00000 187.0810 194.2212
## 44.14286 195.5089 203.2930
## 44.28571 200.9686 209.6750
## 44.42857 223.3283 232.6600
## 44.57143 216.6557 226.7033
## 44.71429 229.0901 240.3876
## 44.85714 224.6049 236.7580
## 45.00000 228.9022 242.0222
## 45.14286 236.7091 250.6220
## 45.28571 238.3741 253.1053
## 45.42857 251.0398 266.5021
## 45.57143 248.5850 264.7738
## 
## 
## $東北地方
## $東北地方$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 30.10492 21.03076 27.02625 27.07445 26.01309 22.34499 24.70651 21.70127
##  [9] 24.22301 23.76492 22.38912 24.28135 24.12873 24.33404
## 
## $東北地方$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%       95%
## 43.71429 23.76260 20.405182
## 43.85714 14.42281 10.924778
## 44.00000 20.31013 16.754833
## 44.14286 20.35000 16.790284
## 44.28571 19.06530 15.387367
## 44.42857 15.30696 11.581257
## 44.57143 17.39317 13.521724
## 44.71429 14.03373  9.974778
## 44.85714 16.21633 11.977845
## 45.00000 15.52883 11.168910
## 45.14286 13.87450  9.367132
## 45.28571 15.50512 10.859270
## 45.42857 15.06591 10.268348
## 45.57143 15.00092 10.060274
## 
## $東北地方$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 36.44723 39.80465
## 43.85714 27.63870 31.13674
## 44.00000 33.74237 37.29767
## 44.14286 33.79891 37.35862
## 44.28571 32.96087 36.63881
## 44.42857 29.38301 33.10872
## 44.57143 32.01985 35.89129
## 44.71429 29.36881 33.42776
## 44.85714 32.22969 36.46817
## 45.00000 32.00102 36.36094
## 45.14286 30.90374 35.41110
## 45.28571 33.05758 37.70343
## 45.42857 33.19155 37.98912
## 45.57143 33.66715 38.60780
## 
## 
## $関東地方
## $関東地方$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 271.2727 374.0127 376.9937 485.9208 517.9407 525.0264 366.9697 272.5036
##  [9] 398.7879 388.8388 506.5946 527.4616 542.2468 378.9853
## 
## $関東地方$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 209.1984 176.3382
## 43.85714 295.5727 254.0490
## 44.00000 289.6909 243.4756
## 44.14286 393.9233 345.2227
## 44.28571 423.0199 372.7720
## 44.42857 428.9611 378.1072
## 44.57143 267.2562 214.4710
## 44.71429 163.9255 106.4478
## 44.85714 281.6554 219.6491
## 45.00000 266.6406 201.9528
## 45.14286 380.7658 314.1561
## 45.28571 398.4238 330.1153
## 45.42857 411.4590 342.2241
## 45.57143 245.1082 174.2380
## 
## $関東地方$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 333.3470 366.2072
## 43.85714 452.4527 493.9763
## 44.00000 464.2965 510.5118
## 44.14286 577.9183 626.6188
## 44.28571 612.8614 663.1094
## 44.42857 621.0917 671.9457
## 44.57143 466.6832 519.4683
## 44.71429 381.0818 438.5595
## 44.85714 515.9204 577.9266
## 45.00000 511.0369 575.7247
## 45.14286 632.4234 699.0332
## 45.28571 656.4995 724.8080
## 45.42857 673.0346 742.2694
## 45.57143 512.8623 583.7325
## 
## 
## $中部地方
## $中部地方$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 112.6070 137.2931 121.4380 143.9822 140.0229 175.9477 135.3751 135.6938
##  [9] 151.8480 137.4035 150.7818 157.9275 174.8481 154.5278
## 
## $中部地方$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%       95%
## 43.71429  94.52071  84.94642
## 43.85714 114.41366 102.30204
## 44.00000  93.50001  78.71055
## 44.14286 111.40110  94.15370
## 44.28571 104.52164  85.72838
## 44.42857 136.57478 115.73204
## 44.57143  93.10821  70.73347
## 44.71429  87.62480  62.17862
## 44.85714  98.61747  70.43893
## 45.00000  79.93570  49.51411
## 45.14286  88.77539  55.95116
## 45.28571  92.08845  57.23534
## 45.42857 105.33518  68.53727
## 45.57143  81.29410  42.52652
## 
## $中部地方$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 130.6932 140.2675
## 43.85714 160.1724 172.2841
## 44.00000 149.3759 164.1654
## 44.14286 176.5633 193.8107
## 44.28571 175.5242 194.3175
## 44.42857 215.3205 236.1633
## 44.57143 177.6420 200.0167
## 44.71429 183.7627 209.2089
## 44.85714 205.0785 233.2571
## 45.00000 194.8712 225.2928
## 45.14286 212.7883 245.6125
## 45.28571 223.7666 258.6197
## 45.42857 244.3610 281.1589
## 45.57143 227.7615 266.5291
## 
## 
## $近畿地方
## $近畿地方$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 158.9203 264.3266 204.7156 249.4841 282.2076 278.7727 226.9622 183.9777
##  [9] 265.9598 229.8444 255.1745 279.1179 273.9514 230.9842
## 
## $近畿地方$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 127.8795 111.4474
## 43.85714 228.6498 209.7636
## 44.00000 166.9239 146.9183
## 44.14286 208.6469 187.0289
## 44.28571 239.4830 216.8660
## 44.42857 233.8137 210.0138
## 44.57143 180.3588 155.6884
## 44.71429 128.5958  99.2783
## 44.85714 206.5675 175.1271
## 45.00000 167.5430 134.5627
## 45.14286 189.8200 155.2234
## 45.28571 211.3060 175.4085
## 45.42857 203.6999 166.5110
## 45.57143 158.6477 120.3550
## 
## $近畿地方$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 189.9612 206.3932
## 43.85714 300.0034 318.8895
## 44.00000 242.5072 262.5129
## 44.14286 290.3214 311.9393
## 44.28571 324.9322 347.5492
## 44.42857 323.7317 347.5316
## 44.57143 273.5657 298.2360
## 44.71429 239.3597 268.6771
## 44.85714 325.3520 356.7924
## 45.00000 292.1458 325.1262
## 45.14286 320.5290 355.1256
## 45.28571 346.9299 382.8274
## 45.42857 344.2030 381.3919
## 45.57143 303.3207 341.6133
## 
## 
## $中国地方
## $中国地方$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 11.40893 11.76833 11.76833 11.76833 11.76833 11.76833 11.76833 11.76833
##  [9] 11.76833 11.76833 11.76833 11.76833 11.76833 11.76833
## 
## $中国地方$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%       95%
## 43.71429 2.3822899 -2.396127
## 43.85714 2.3360320 -2.657125
## 44.00000 2.1489836 -2.943191
## 44.14286 1.9655037 -3.223799
## 44.28571 1.7853954 -3.499251
## 44.42857 1.6084795 -3.769821
## 44.57143 1.4345920 -4.035759
## 44.71429 1.2635825 -4.297295
## 44.85714 1.0953126 -4.554642
## 45.00000 0.9296549 -4.807993
## 45.14286 0.7664912 -5.057531
## 45.28571 0.6057122 -5.303421
## 45.42857 0.4472163 -5.545820
## 45.57143 0.2909089 -5.784871
## 
## $中国地方$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 20.43558 25.21400
## 43.85714 21.20063 26.19379
## 44.00000 21.38768 26.47985
## 44.14286 21.57116 26.76046
## 44.28571 21.75127 27.03591
## 44.42857 21.92818 27.30648
## 44.57143 22.10207 27.57242
## 44.71429 22.27308 27.83396
## 44.85714 22.44135 28.09130
## 45.00000 22.60701 28.34465
## 45.14286 22.77017 28.59419
## 45.28571 22.93095 28.84008
## 45.42857 23.08944 29.08248
## 45.57143 23.24575 29.32153
## 
## 
## $四国地方
## $四国地方$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 1.515110 1.324678 1.324678 1.324678 1.324678 1.324678 1.324678 1.324678
##  [9] 1.324678 1.324678 1.324678 1.324678 1.324678 1.324678
## 
## $四国地方$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%       95%
## 43.71429 -1.968750 -3.812994
## 43.85714 -2.415108 -4.394831
## 44.00000 -2.486249 -4.503632
## 44.14286 -2.556087 -4.610439
## 44.28571 -2.624689 -4.715358
## 44.42857 -2.692120 -4.818485
## 44.57143 -2.758438 -4.919909
## 44.71429 -2.823696 -5.019712
## 44.85714 -2.887942 -5.117969
## 45.00000 -2.951224 -5.214749
## 45.14286 -3.013583 -5.310119
## 45.28571 -3.075057 -5.404136
## 45.42857 -3.135685 -5.496859
## 45.57143 -3.195500 -5.588337
## 
## $四国地方$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 4.998970 6.843214
## 43.85714 5.064463 7.044186
## 44.00000 5.135604 7.152987
## 44.14286 5.205442 7.259794
## 44.28571 5.274044 7.364713
## 44.42857 5.341475 7.467840
## 44.57143 5.407793 7.569264
## 44.71429 5.473051 7.669067
## 44.85714 5.537297 7.767324
## 45.00000 5.600579 7.864105
## 45.14286 5.662938 7.959474
## 45.28571 5.724413 8.053492
## 45.42857 5.785040 8.146214
## 45.57143 5.844855 8.237692
## 
## 
## $九州地方
## $九州地方$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 47.13733 59.64062 60.94604 63.65945 64.12882 61.39700 62.61917 58.54826
##  [9] 65.59485 64.52613 68.01372 68.25423 65.25113 66.62425
## 
## $九州地方$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%         95%
## 43.71429 25.133893  13.4859782
## 43.85714 33.006071  18.9065903
## 44.00000 31.140591  15.3625404
## 44.14286 32.898308  16.6143427
## 44.28571 31.304019  13.9276200
## 44.42857 24.365957   4.7629098
## 44.57143 21.813187   0.2118046
## 44.71429 11.548236 -13.3320725
## 44.85714 13.970213 -13.3582187
## 45.00000  8.831835 -20.6509458
## 45.14286  9.077278 -22.1217890
## 45.28571  5.900581 -27.1074467
## 45.42857 -0.855050 -35.8495493
## 45.57143 -3.032602 -39.9067123
## 
## $九州地方$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%       95%
## 43.71429  69.14076  80.78868
## 43.85714  86.27518 100.37466
## 44.00000  90.75149 106.52954
## 44.14286  94.42060 110.70456
## 44.28571  96.95362 114.33002
## 44.42857  98.42803 118.03108
## 44.57143 103.42515 125.02653
## 44.71429 105.54828 130.42859
## 44.85714 117.21949 144.54792
## 45.00000 120.22043 149.70321
## 45.14286 126.95016 158.14923
## 45.28571 130.60788 163.61591
## 45.42857 131.35732 166.35181
## 45.57143 136.28110 173.15521

　

都道府県別

x_by_prefs %>% 
  dplyr::select(Pref, n) %>% 
  split(.$Pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道
## $北海道$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 184.2972 159.7512 173.5928 180.8045 184.5220 205.7002 197.6753 207.7486
##  [9] 201.6470 204.1179 210.4270 210.5461 221.8307 218.0035
## 
## $北海道$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 174.6240 169.5033
## 43.85714 148.9590 143.2459
## 44.00000 160.1047 152.9645
## 44.14286 166.1002 158.3161
## 44.28571 168.0754 159.3691
## 44.42857 188.0720 178.7403
## 44.57143 178.6949 168.6472
## 44.71429 186.4072 175.1097
## 44.85714 178.6890 166.5359
## 45.00000 179.3337 166.2137
## 45.14286 184.1450 170.2321
## 45.28571 182.7181 167.9868
## 45.42857 192.6217 177.1593
## 45.57143 187.4221 171.2333
## 
## $北海道$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 193.9704 199.0911
## 43.85714 170.5435 176.2566
## 44.00000 187.0810 194.2212
## 44.14286 195.5089 203.2930
## 44.28571 200.9686 209.6750
## 44.42857 223.3283 232.6600
## 44.57143 216.6557 226.7033
## 44.71429 229.0901 240.3876
## 44.85714 224.6049 236.7580
## 45.00000 228.9022 242.0222
## 45.14286 236.7091 250.6220
## 45.28571 238.3741 253.1053
## 45.42857 251.0398 266.5021
## 45.57143 248.5850 264.7738
## 
## 
## $青森県
## $青森県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 6.317046 3.242146 5.443528 3.867515 4.995814 4.188043 4.766342 4.352326
##  [9] 4.648728 4.436528 4.588447 4.479685 4.557550 4.501805
## 
## $青森県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                 80%        95%
## 43.71429 3.78818935  2.4494934
## 43.85714 0.29764428 -1.2610809
## 44.00000 2.44354741  0.8554535
## 44.14286 0.62023272 -1.0987748
## 44.28571 1.65981339 -0.1061587
## 44.42857 0.67317741 -1.1874798
## 44.57143 1.14741555 -0.7683285
## 44.71429 0.58768194 -1.4052002
## 44.85714 0.77530867 -1.2751559
## 45.00000 0.43572105 -1.6821783
## 45.14286 0.47936221 -1.6958556
## 45.28571 0.25460442 -1.9820182
## 45.42857 0.22694169 -2.0655436
## 45.57143 0.06305167 -2.2866823
## 
## $青森県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%       95%
## 43.71429 8.845903 10.184599
## 43.85714 6.186648  7.745373
## 44.00000 8.443509 10.031603
## 44.14286 7.114798  8.833805
## 44.28571 8.331815 10.097787
## 44.42857 7.702908  9.563565
## 44.57143 8.385269 10.301013
## 44.71429 8.116970 10.109852
## 44.85714 8.522148 10.572613
## 45.00000 8.437335 10.555234
## 45.14286 8.697531 10.872749
## 45.28571 8.704766 10.941388
## 45.42857 8.888157 11.180643
## 45.57143 8.940558 11.290292
## 
## 
## $岩手県
## $岩手県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 0.18679902 0.68256526 0.18679803 0.15797389 0.51196179 0.18090569
##  [7] 0.18677210 0.23590444 0.07260846 0.23590462 0.24107369 0.13866285
## [13] 0.23700038 0.23590948
## 
## $岩手県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                  80%        95%
## 43.71429 -0.39954437 -0.7099358
## 43.85714  0.09552469 -0.2152358
## 44.00000 -0.40093890 -0.7120680
## 44.14286 -0.43045857 -0.7419559
## 44.28571 -0.07716539 -0.3890305
## 44.42857 -0.40891538 -0.7211478
## 44.57143 -0.40374205 -0.7163414
## 44.71429 -0.38223829 -0.7094633
## 44.85714 -0.54658870 -0.8743719
## 45.00000 -0.38434519 -0.7126856
## 45.14286 -0.38022698 -0.7091237
## 45.28571 -0.48368691 -0.8131390
## 45.42857 -0.38639671 -0.7164032
## 45.57143 -0.38853317 -0.7190931
## 
## $岩手県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%      95%
## 43.71429 0.7731424 1.083534
## 43.85714 1.2696058 1.580366
## 44.00000 0.7745350 1.085664
## 44.14286 0.7464064 1.057904
## 44.28571 1.1010890 1.412954
## 44.42857 0.7707268 1.082959
## 44.57143 0.7772862 1.089886
## 44.71429 0.8540472 1.181272
## 44.85714 0.6918056 1.019589
## 45.00000 0.8561544 1.184495
## 45.14286 0.8623744 1.191271
## 45.28571 0.7610126 1.090465
## 45.42857 0.8603975 1.190404
## 45.57143 0.8603521 1.190912
## 
## 
## $宮城県
## $宮城県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 16.89894 15.10586 16.48388 15.42484 16.23874 15.61324 16.09395 15.72451
##  [9] 16.00843 15.79024 15.95793 15.82905 15.92810 15.85198
## 
## $宮城県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%       95%
## 43.71429 12.452403 10.098548
## 43.85714 10.616567  8.240077
## 44.00000 11.586775  8.994403
## 44.14286 10.440596  7.802095
## 44.28571 10.966582  8.175671
## 44.42857 10.225315  7.373119
## 44.57143 10.479758  7.507784
## 44.71429  9.979511  6.938291
## 44.85714 10.072167  6.929696
## 45.00000  9.716944  6.501938
## 45.14286  9.714943  6.410107
## 45.28571  9.447941  6.069985
## 45.42857  9.391611  5.931406
## 45.57143  9.178999  5.646536
## 
## $宮城県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 21.34548 23.69933
## 43.85714 19.59516 21.97165
## 44.00000 21.38099 23.97336
## 44.14286 20.40909 23.04759
## 44.28571 21.51090 24.30181
## 44.42857 21.00117 23.85336
## 44.57143 21.70814 24.68012
## 44.71429 21.46952 24.51074
## 44.85714 21.94470 25.08717
## 45.00000 21.86353 25.07853
## 45.14286 22.20091 25.50575
## 45.28571 22.21016 25.58812
## 45.42857 22.46458 25.92478
## 45.57143 22.52496 26.05742
## 
## 
## $秋田県
## $秋田県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 0.1958440 0.2238924 0.2238924 0.2238924 0.2238924 0.2238924 0.2238924
##  [8] 0.2238924 0.2238924 0.2238924 0.2238924 0.2238924 0.2238924 0.2238924
## 
## $秋田県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%       95%
## 43.71429 -1.035624 -1.687524
## 43.85714 -1.019091 -1.677087
## 44.00000 -1.019091 -1.677087
## 44.14286 -1.019091 -1.677087
## 44.28571 -1.019091 -1.677087
## 44.42857 -1.019091 -1.677087
## 44.57143 -1.019091 -1.677087
## 44.71429 -1.019091 -1.677087
## 44.85714 -1.019091 -1.677087
## 45.00000 -1.019091 -1.677087
## 45.14286 -1.019091 -1.677087
## 45.28571 -1.019091 -1.677087
## 45.42857 -1.019091 -1.677087
## 45.57143 -1.019091 -1.677087
## 
## $秋田県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 1.427312 2.079212
## 43.85714 1.466876 2.124871
## 44.00000 1.466876 2.124871
## 44.14286 1.466876 2.124871
## 44.28571 1.466876 2.124871
## 44.42857 1.466876 2.124871
## 44.57143 1.466876 2.124871
## 44.71429 1.466876 2.124871
## 44.85714 1.466876 2.124871
## 45.00000 1.466876 2.124871
## 45.14286 1.466876 2.124871
## 45.28571 1.466876 2.124871
## 45.42857 1.466876 2.124871
## 45.57143 1.466876 2.124871
## 
## 
## $山形県
## $山形県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 0.07575694 0.22508140 0.26642104 0.30184071 0.36170023 0.39754598
##  [7] 0.21187072 0.41545043 0.42301220 0.42723857 0.42852070 0.42724686
## [13] 0.42379598 0.41853232
## 
## $山形県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                 80%       95%
## 43.71429 -0.8506905 -1.341122
## 43.85714 -0.7167279 -1.215292
## 44.00000 -0.6996779 -1.211100
## 44.14286 -0.6963219 -1.224717
## 44.28571 -0.6742561 -1.222658
## 44.42857 -0.6796046 -1.249814
## 44.57143 -0.9076705 -1.500320
## 44.71429 -0.7093884 -1.304842
## 44.85714 -0.7336615 -1.345968
## 45.00000 -0.7565858 -1.383265
## 45.14286 -0.7779521 -1.416620
## 45.28571 -0.7976910 -1.446134
## 45.42857 -0.8158333 -1.472053
## 45.57143 -0.8324771 -1.494722
## 
## $山形県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 1.002204 1.492636
## 43.85714 1.166891 1.665454
## 44.00000 1.232520 1.743942
## 44.14286 1.300003 1.828399
## 44.28571 1.397657 1.946059
## 44.42857 1.474697 2.044906
## 44.57143 1.331412 1.924061
## 44.71429 1.540289 2.135743
## 44.85714 1.579686 2.191992
## 45.00000 1.611063 2.237742
## 45.14286 1.634993 2.273662
## 45.28571 1.652185 2.300628
## 45.42857 1.663425 2.319645
## 45.57143 1.669542 2.331786
## 
## 
## $福島県
## $福島県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 2.600029 4.463305 2.533452 3.651680 2.716438 2.963369 2.977409 3.116846
##  [9] 4.543086 3.033600 4.044771 3.242025 3.401207 3.326976
## 
## $福島県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                  80%        95%
## 43.71429  0.07777105 -1.2574318
## 43.85714  1.86618315  0.4913499
## 44.00000 -0.13643559 -1.5497885
## 44.14286  0.91095865 -0.5398916
## 44.28571 -0.09333233 -1.5807348
## 44.42857  0.08620625 -1.4368716
## 44.57143  0.03439681 -1.5235396
## 44.71429  0.01850601 -1.6216563
## 44.85714  1.36557329 -0.3165002
## 45.00000 -0.22115905 -1.9441245
## 45.14286  0.71455646 -1.0483527
## 45.28571 -0.16197278 -1.9639404
## 45.42857 -0.07500890 -1.9152062
## 45.57143 -0.21998648 -2.0976352
## 
## $福島県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 5.122288 6.457491
## 43.85714 7.060427 8.435260
## 44.00000 5.203339 6.616692
## 44.14286 6.392401 7.843251
## 44.28571 5.526208 7.013611
## 44.42857 5.840531 7.363609
## 44.57143 5.920421 7.478357
## 44.71429 6.215187 7.855349
## 44.85714 7.720598 9.402672
## 45.00000 6.288359 8.011325
## 45.14286 7.374986 9.137895
## 45.28571 6.646023 8.447991
## 45.42857 6.877422 8.717619
## 45.57143 6.873939 8.751588
## 
## 
## $茨城県
## $茨城県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 8.084251 7.996803 8.144842 8.734561 8.434246 8.762462 8.980983 8.582003
##  [9] 8.582003 8.582003 8.582003 8.582003 8.582003 8.582003
## 
## $茨城県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 4.652522 2.835874
## 43.85714 4.447201 2.568155
## 44.00000 4.481159 2.541722
## 44.14286 4.960242 2.962239
## 44.28571 4.552445 2.497543
## 44.42857 4.776074 2.665808
## 44.57143 4.892684 2.728469
## 44.71429 4.307350 2.044486
## 44.85714 4.192544 1.868905
## 45.00000 4.080664 1.697800
## 45.14286 3.971499 1.530846
## 45.28571 3.864860 1.367755
## 45.42857 3.760578 1.208270
## 45.57143 3.658505 1.052163
## 
## $茨城県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 11.51598 13.33263
## 43.85714 11.54640 13.42545
## 44.00000 11.80853 13.74796
## 44.14286 12.50888 14.50688
## 44.28571 12.31605 14.37095
## 44.42857 12.74885 14.85912
## 44.57143 13.06928 15.23350
## 44.71429 12.85665 15.11952
## 44.85714 12.97146 15.29510
## 45.00000 13.08334 15.46621
## 45.14286 13.19251 15.63316
## 45.28571 13.29915 15.79625
## 45.42857 13.40343 15.95573
## 45.57143 13.50550 16.11184
## 
## 
## $栃木県
## $栃木県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 1.511421 1.378995 1.433404 1.391071 1.661793 1.675878 1.359721 1.644207
##  [9] 1.590840 1.657516 1.650226 1.666333 1.665982 1.670003
## 
## $栃木県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%       95%
## 43.71429 -1.432690 -2.991208
## 43.85714 -1.600654 -3.177984
## 44.00000 -1.700427 -3.359377
## 44.14286 -1.767257 -3.439175
## 44.28571 -1.531053 -3.221243
## 44.42857 -1.533941 -3.233117
## 44.57143 -1.867629 -3.576085
## 44.71429 -1.726665 -3.511097
## 44.85714 -1.804240 -3.601487
## 45.00000 -1.776061 -3.593687
## 45.14286 -1.804275 -3.632977
## 45.28571 -1.810626 -3.651217
## 45.42857 -1.829817 -3.680380
## 45.57143 -1.844616 -3.705143
## 
## $栃木県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 4.455531 6.014050
## 43.85714 4.358644 5.935975
## 44.00000 4.567235 6.226184
## 44.14286 4.549399 6.221316
## 44.28571 4.854638 6.544828
## 44.42857 4.885697 6.584872
## 44.57143 4.587071 6.295526
## 44.71429 5.015078 6.799510
## 44.85714 4.985920 6.783167
## 45.00000 5.091094 6.908720
## 45.14286 5.104726 6.933428
## 45.28571 5.143292 6.983883
## 45.42857 5.161780 7.012344
## 45.57143 5.184621 7.045148
## 
## 
## $群馬県
## $群馬県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 2.260315 4.174545 4.893488 3.866577 4.646206 4.920087 4.808302 4.701743
##  [9] 5.155171 5.178033 5.286654 5.248659 5.417797 5.401068
## 
## $群馬県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                 80%       95%
## 43.71429 -2.2380517 -4.619343
## 43.85714 -0.9773548 -3.704606
## 44.00000 -0.4615807 -3.296383
## 44.14286 -1.5524867 -4.421166
## 44.28571 -1.0953080 -4.134682
## 44.42857 -0.9204125 -4.012186
## 44.57143 -1.1447219 -4.296062
## 44.71429 -1.3585994 -4.566751
## 44.85714 -1.0466794 -4.329741
## 45.00000 -1.0753421 -4.385679
## 45.14286 -1.0328686 -4.378222
## 45.28571 -1.1136944 -4.481721
## 45.42857 -1.0002489 -4.397757
## 45.57143 -1.0477484 -4.461546
## 
## $群馬県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%       95%
## 43.71429  6.758682  9.139973
## 43.85714  9.326445 12.053696
## 44.00000 10.248556 13.083358
## 44.14286  9.285641 12.154320
## 44.28571 10.387720 13.427094
## 44.42857 10.760587 13.852361
## 44.57143 10.761326 13.912667
## 44.71429 10.762086 13.970237
## 44.85714 11.357022 14.640084
## 45.00000 11.431407 14.741744
## 45.14286 11.606176 14.951529
## 45.28571 11.611012 14.979038
## 45.42857 11.835843 15.233351
## 45.57143 11.849885 15.263682
## 
## 
## $埼玉県
## $埼玉県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 53.63699 53.53257 58.16262 54.77485 88.42464 69.18497 62.04726 61.91460
##  [9] 63.47470 64.94050 63.38261 81.30429 72.44760 67.32398
## 
## $埼玉県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 40.42135 33.42540
## 43.85714 39.77765 32.49623
## 44.00000 43.88879 36.33268
## 44.14286 40.00032 32.17915
## 44.28571 73.16582 65.08829
## 44.42857 53.45678 45.13078
## 44.57143 45.86331 37.29604
## 44.71429 43.46627 33.70031
## 44.85714 44.28878 34.13236
## 45.00000 45.04431 34.51189
## 45.14286 42.80064 31.90520
## 45.28571 60.05867 48.81191
## 45.42857 50.55844 38.97101
## 45.57143 44.80966 32.89130
## 
## $埼玉県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%       95%
## 43.71429  66.85264  73.84858
## 43.85714  67.28749  74.56891
## 44.00000  72.43646  79.99257
## 44.14286  69.54939  77.37055
## 44.28571 103.68345 111.76098
## 44.42857  84.91316  93.23915
## 44.57143  78.23122  86.79848
## 44.71429  80.36293  90.12888
## 44.85714  82.66063  92.81705
## 45.00000  84.83670  95.36911
## 45.14286  83.96458  94.86002
## 45.28571 102.54992 113.79667
## 45.42857  94.33676 105.92419
## 45.57143  89.83829 101.75665
## 
## 
## $千葉県
## $千葉県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 39.65152 44.86902 42.09901 48.46426 43.95170 48.71625 44.39259 43.55540
##  [9] 45.33320 44.19370 47.64134 45.80079 48.06164 46.36469
## 
## $千葉県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 28.23295 22.18832
## 43.85714 32.20777 25.50531
## 44.00000 29.05753 22.15379
## 44.14286 35.05333 27.95401
## 44.28571 30.18123 22.89158
## 44.42857 34.59538 27.12025
## 44.57143 29.92982 22.27369
## 44.71429 28.06623 19.86675
## 44.85714 29.26231 20.75489
## 45.00000 27.68234 18.94176
## 45.14286 30.70098 21.73329
## 45.28571 28.44201 19.25283
## 45.42857 30.29430 20.88885
## 45.57143 28.19798 18.58111
## 
## $千葉県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 51.07009 57.11471
## 43.85714 57.53027 64.23273
## 44.00000 55.14049 62.04423
## 44.14286 61.87519 68.97451
## 44.28571 57.72218 65.01183
## 44.42857 62.83712 70.31226
## 44.57143 58.85537 66.51150
## 44.71429 59.04457 67.24405
## 44.85714 61.40410 69.91152
## 45.00000 60.70505 69.44563
## 45.14286 64.58170 73.54939
## 45.28571 63.15956 72.34874
## 45.42857 65.82897 75.23443
## 45.57143 64.53139 74.14826
## 
## 
## $東京都
## $東京都$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 133.3689 218.6167 188.1429 279.6841 250.6146 283.4794 184.1089 137.4567
##  [9] 224.0753 193.4140 284.7743 255.5300 288.2261 188.6926
## 
## $東京都$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%       95%
## 43.71429  88.26384  64.38668
## 43.85714 167.11553 139.85246
## 44.00000 133.99985 105.33828
## 44.14286 223.18876 193.28193
## 44.28571 192.01068 160.98765
## 44.42857 222.97541 190.94653
## 44.57143 121.88529  88.94612
## 44.71429  68.51893  32.02547
## 44.85714 151.66028 113.32609
## 45.00000 118.57901  78.96374
## 45.14286 207.75107 166.97743
## 45.28571 176.52085 134.69592
## 45.42857 207.40894 164.62695
## 45.57143 106.22522  62.56964
## 
## $東京都$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 178.4739 202.3511
## 43.85714 270.1179 297.3809
## 44.00000 242.2859 270.9474
## 44.14286 336.1794 366.0863
## 44.28571 309.2185 340.2415
## 44.42857 343.9834 376.0123
## 44.57143 246.3325 279.2716
## 44.71429 206.3945 242.8880
## 44.85714 296.4903 334.8245
## 45.00000 268.2491 307.8643
## 45.14286 361.7975 402.5712
## 45.28571 334.5392 376.3641
## 45.42857 369.0432 411.8252
## 45.57143 271.1599 314.8155
## 
## 
## $神奈川県
## $神奈川県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1]  60.11877  79.03298  64.99019  95.79328  90.23958 106.01084  81.52786
##  [8]  75.28760  81.02556  76.15565  89.07372  88.86774  94.86825  82.87319
## 
## $神奈川県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 40.56098 30.20771
## 43.85714 57.33474 45.84838
## 44.00000 42.59918 30.74609
## 44.14286 73.03856 60.99293
## 44.28571 67.21868 55.03216
## 44.42857 82.75793 70.44859
## 44.57143 58.05647 45.63147
## 44.71429 49.17158 35.34659
## 44.85714 53.95262 39.62106
## 45.00000 48.53174 33.90853
## 45.14286 61.02501 46.17692
## 45.28571 60.44037 45.39182
## 45.42857 66.08164 50.84293
## 45.57143 53.73715 38.31346
## 
## $神奈川県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%       95%
## 43.71429  79.67656  90.02983
## 43.85714 100.73122 112.21758
## 44.00000  87.38119  99.23428
## 44.14286 118.54801 130.59363
## 44.28571 113.26047 125.44699
## 44.42857 129.26374 141.57308
## 44.57143 104.99925 117.42425
## 44.71429 101.40362 115.22861
## 44.85714 108.09851 122.43006
## 45.00000 103.77956 118.40277
## 45.14286 117.12243 131.97052
## 45.28571 117.29512 132.34366
## 45.42857 123.65485 138.89356
## 45.57143 112.00923 127.43292
## 
## 
## $新潟県
## $新潟県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 2.4569109 2.1119786 1.8310117 1.8852348 1.5688295 1.5504363 2.2176903
##  [8] 1.4156095 1.2804183 1.1702639 1.0805095 1.0073772 0.9477887 0.8992356
## 
## $新潟県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                   80%        95%
## 43.71429  1.019346892  0.2583465
## 43.85714  0.629242268 -0.1556709
## 44.00000  0.319030480 -0.4813640
## 44.14286  0.354146178 -0.4563632
## 44.28571  0.025185961 -0.7919696
## 44.42857 -0.001486435 -0.8230247
## 44.57143  0.660295228 -0.1641399
## 44.71429 -0.175734163 -1.0181406
## 44.85714 -0.317279700 -1.1630499
## 45.00000 -0.431638904 -1.2796350
## 45.14286 -0.524178757 -1.3736494
## 45.28571 -0.599157680 -1.4496059
## 45.42857 -0.659971042 -1.5110676
## 45.57143 -0.709336722 -1.5608635
## 
## $新潟県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 3.894475 4.655475
## 43.85714 3.594715 4.379628
## 44.00000 3.342993 4.143388
## 44.14286 3.416323 4.226833
## 44.28571 3.112473 3.929629
## 44.42857 3.102359 3.923897
## 44.57143 3.775085 4.599520
## 44.71429 3.006953 3.849360
## 44.85714 2.878116 3.723887
## 45.00000 2.772167 3.620163
## 45.14286 2.685198 3.534668
## 45.28571 2.613912 3.464360
## 45.42857 2.555548 3.406645
## 45.57143 2.507808 3.359335
## 
## 
## $富山県
## $富山県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 0.17752023 0.06038183 0.06650032 0.05970559 0.07633631 0.06017882
##  [7] 0.05925393 0.05555953 0.05506317 0.05095777 0.04882220 0.04631705
## [13] 0.04432780 0.04201569
## 
## $富山県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%       95%
## 43.71429 -2.304320 -3.618127
## 43.85714 -2.561099 -3.948827
## 44.00000 -2.714962 -4.187379
## 44.14286 -2.844307 -4.381599
## 44.28571 -3.017572 -4.655387
## 44.42857 -3.146083 -4.843375
## 44.57143 -3.252297 -5.005326
## 44.71429 -3.345869 -5.146477
## 44.85714 -3.432391 -5.278538
## 45.00000 -3.508520 -5.392793
## 45.14286 -3.575509 -5.494113
## 45.28571 -3.635406 -5.584392
## 45.42857 -3.689189 -5.665593
## 45.57143 -3.737428 -5.738144
## 
## $富山県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 2.659361 3.973168
## 43.85714 2.681863 4.069591
## 44.00000 2.847963 4.320380
## 44.14286 2.963718 4.501010
## 44.28571 3.170244 4.808060
## 44.42857 3.266440 4.963733
## 44.57143 3.370805 5.123834
## 44.71429 3.456989 5.257596
## 44.85714 3.542517 5.388664
## 45.00000 3.610435 5.494709
## 45.14286 3.673153 5.591758
## 45.28571 3.728040 5.677026
## 45.42857 3.777844 5.754249
## 45.57143 3.821459 5.822175
## 
## 
## $石川県
## $石川県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 2.207621 2.207621 2.207621 2.207621 2.207621 2.207621 2.207621 2.207621
##  [9] 2.207621 2.207621 2.207621 2.207621 2.207621 2.207621
## 
## $石川県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%       95%
## 43.71429 -1.507603 -3.474324
## 43.85714 -1.717152 -3.794802
## 44.00000 -1.916067 -4.099015
## 44.14286 -2.105818 -4.389214
## 44.28571 -2.287566 -4.667174
## 44.42857 -2.462246 -4.934325
## 44.57143 -2.630624 -5.191836
## 44.71429 -2.793336 -5.440682
## 44.85714 -2.950918 -5.681683
## 45.00000 -3.103827 -5.915537
## 45.14286 -3.252455 -6.142845
## 45.28571 -3.397144 -6.364127
## 45.42857 -3.538190 -6.579838
## 45.57143 -3.675855 -6.790380
## 
## $石川県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%       95%
## 43.71429 5.922845  7.889565
## 43.85714 6.132394  8.210043
## 44.00000 6.331308  8.514256
## 44.14286 6.521059  8.804456
## 44.28571 6.702808  9.082416
## 44.42857 6.877488  9.349566
## 44.57143 7.045866  9.607078
## 44.71429 7.208577  9.855924
## 44.85714 7.366159 10.096925
## 45.00000 7.519068 10.330779
## 45.14286 7.667697 10.558086
## 45.28571 7.812385 10.779368
## 45.42857 7.953431 10.995079
## 45.57143 8.091097 11.205621
## 
## 
## $福井県
## $福井県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 0.9100675 1.0368217 1.0116204 0.9898843 0.9711371 0.9549678 0.9410218
##  [8] 0.9289934 0.9186190 0.9096712 0.9019537 0.8952974 0.8895564 0.8846048
## 
## $福井県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                 80%       95%
## 43.71429 -0.9708915 -1.966611
## 43.85714 -0.9321870 -1.974517
## 44.00000 -1.1926987 -2.359595
## 44.14286 -1.3743386 -2.625883
## 44.28571 -1.5053497 -2.816323
## 44.42857 -1.6018366 -2.955327
## 44.57143 -1.6739308 -3.058203
## 44.71429 -1.7283903 -3.135124
## 44.85714 -1.7698948 -3.193108
## 45.00000 -1.8017684 -3.237118
## 45.14286 -1.8264153 -3.270727
## 45.28571 -1.8455975 -3.296540
## 45.42857 -1.8606195 -3.316475
## 45.57143 -1.8724550 -3.331954
## 
## $福井県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 2.791026 3.786746
## 43.85714 3.005830 4.048161
## 44.00000 3.215939 4.382836
## 44.14286 3.354107 4.605651
## 44.28571 3.447624 4.758597
## 44.42857 3.511772 4.865263
## 44.57143 3.555974 4.940247
## 44.71429 3.586377 4.993111
## 44.85714 3.607133 5.030346
## 45.00000 3.621111 5.056460
## 45.14286 3.630323 5.074634
## 45.28571 3.636192 5.087134
## 45.42857 3.639732 5.095588
## 45.57143 3.641665 5.101164
## 
## 
## $山梨県
## $山梨県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 1.509151 1.480129 2.030532 2.104600 2.173060 2.388059 1.428783 1.755717
##  [9] 1.587441 1.922397 2.141481 2.115495 2.084569 1.678572
## 
## $山梨県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                  80%        95%
## 43.71429  0.02453019 -0.7613808
## 43.85714 -0.15627607 -1.0225364
## 44.00000  0.34844930 -0.5419915
## 44.14286  0.39984317 -0.5026006
## 44.28571  0.45218342 -0.4587938
## 44.42857  0.65328012 -0.2650562
## 44.57143 -0.31905213 -1.2443004
## 44.71429 -0.05521609 -1.0138663
## 44.85714 -0.25368105 -1.2283119
## 45.00000  0.06028658 -0.9254552
## 45.14286  0.26147047 -0.7337468
## 45.28571  0.21876276 -0.7853067
## 45.42857  0.17162830 -0.8410212
## 45.57143 -0.25030408 -1.2713897
## 
## $山梨県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 2.993773 3.779684
## 43.85714 3.116534 3.982794
## 44.00000 3.712615 4.603056
## 44.14286 3.809357 4.711801
## 44.28571 3.893937 4.804914
## 44.42857 4.122837 5.041173
## 44.57143 3.176619 4.101867
## 44.71429 3.566651 4.525301
## 44.85714 3.428562 4.403193
## 45.00000 3.784508 4.770250
## 45.14286 4.021491 5.016708
## 45.28571 4.012227 5.016297
## 45.42857 3.997509 5.010159
## 45.57143 3.607449 4.628535
## 
## 
## $長野県
## $長野県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 4.787287 4.787287 4.787287 4.787287 4.787287 4.787287 4.787287 4.787287
##  [9] 4.787287 4.787287 4.787287 4.787287 4.787287 4.787287
## 
## $長野県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%          95%
## 43.71429 2.1282191  0.720593476
## 43.85714 2.0023180  0.528044281
## 44.00000 1.8818674  0.343831040
## 44.14286 1.7662154  0.166956579
## 44.28571 1.6548304 -0.003392027
## 44.42857 1.5472724 -0.167887858
## 44.57143 1.4431720 -0.327095682
## 44.71429 1.3422158 -0.481494886
## 44.85714 1.2441350 -0.631496526
## 45.00000 1.1486971 -0.777456233
## 45.14286 1.0556992 -0.919684140
## 45.28571 0.9649634 -1.058452641
## 45.42857 0.8763321 -1.194002518
## 45.57143 0.7896653 -1.326547856
## 
## $長野県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%       95%
## 43.71429 7.446355  8.853981
## 43.85714 7.572256  9.046530
## 44.00000 7.692707  9.230743
## 44.14286 7.808359  9.407618
## 44.28571 7.919744  9.577966
## 44.42857 8.027302  9.742462
## 44.57143 8.131402  9.901670
## 44.71429 8.232359 10.056069
## 44.85714 8.330439 10.206071
## 45.00000 8.425877 10.352031
## 45.14286 8.518875 10.494259
## 45.28571 8.609611 10.633027
## 45.42857 8.698242 10.768577
## 45.57143 8.784909 10.901122
## 
## 
## $岐阜県
## $岐阜県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 7.044331 6.774716 6.010011 8.013714 9.439578 6.583805 7.153403 7.746412
##  [9] 7.625559 7.275141 8.031637 8.763069 7.249607 7.722250
## 
## $岐阜県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%        95%
## 43.71429 3.7589529  2.0197789
## 43.85714 2.9921784  0.9898239
## 44.00000 1.9596588 -0.1844680
## 44.14286 3.7538111  1.4987545
## 44.28571 4.9913109  2.6365403
## 44.42857 1.9580680 -0.4906492
## 44.57143 2.3576964 -0.1809975
## 44.71429 2.5303084 -0.2309303
## 44.85714 2.1353128 -0.7710479
## 45.00000 1.5534202 -1.4754760
## 45.14286 2.0957411 -1.0465325
## 45.28571 2.6228536 -0.6275801
## 45.42857 0.9123527 -2.4423869
## 45.57143 1.1941069 -2.2616836
## 
## $岐阜県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 10.32971 12.06888
## 43.85714 10.55725 12.55961
## 44.00000 10.06036 12.20449
## 44.14286 12.27362 14.52867
## 44.28571 13.88785 16.24262
## 44.42857 11.20954 13.65826
## 44.57143 11.94911 14.48780
## 44.71429 12.96252 15.72376
## 44.85714 13.11580 16.02217
## 45.00000 12.99686 16.02576
## 45.14286 13.96753 17.10981
## 45.28571 14.90328 18.15372
## 45.42857 13.58686 16.94160
## 45.57143 14.25039 17.70618
## 
## 
## $静岡県
## $静岡県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 15.41376 14.98255 18.53235 16.03735 18.33265 17.22198 16.92285 14.88926
##  [9] 18.10531 15.64855 17.22849 17.68537 20.27673 17.28596
## 
## $静岡県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%       95%
## 43.71429 11.610632  9.597376
## 43.85714 10.602491  8.283830
## 44.00000 13.778172 11.261462
## 44.14286 11.120761  8.518075
## 44.28571 13.108495 10.342996
## 44.42857 11.832569  8.979588
## 44.57143 11.270237  8.277924
## 44.71429  8.983573  5.857292
## 44.85714 11.916733  8.640699
## 45.00000  9.261907  5.881023
## 45.14286 10.610264  7.106788
## 45.28571 10.876488  7.272082
## 45.42857 13.254778  9.537582
## 45.57143 10.080417  6.266033
## 
## $静岡県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 19.21690 21.23015
## 43.85714 19.36260 21.68126
## 44.00000 23.28653 25.80324
## 44.14286 20.95394 23.55663
## 44.28571 23.55680 26.32230
## 44.42857 22.61139 25.46437
## 44.57143 22.57546 25.56777
## 44.71429 20.79494 23.92122
## 44.85714 24.29388 27.56992
## 45.00000 22.03519 25.41608
## 45.14286 23.84671 27.35019
## 45.28571 24.49426 28.09867
## 45.42857 27.29868 31.01588
## 45.57143 24.49150 28.30589
## 
## 
## $愛知県
## $愛知県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 74.92974 82.54168 73.45984 76.71011 90.52578 92.07316 88.01512 84.64652
##  [9] 76.17121 80.04374 83.26994 86.52889 86.45705 86.23702
## 
## $愛知県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 61.27568 54.04766
## 43.85714 66.11910 57.42552
## 44.00000 54.35547 44.24222
## 44.14286 55.46263 44.21489
## 44.28571 67.77615 55.73322
## 44.42857 68.22796 55.60507
## 44.57143 63.14624 49.98144
## 44.71429 57.16021 42.60984
## 44.85714 46.35820 30.57615
## 45.00000 47.80631 30.74085
## 45.14286 48.95992 30.79730
## 45.28571 50.67076 31.68862
## 45.42857 49.41021 29.79879
## 45.57143 48.06130 27.85229
## 
## $愛知県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%       95%
## 43.71429  88.58380  95.81182
## 43.85714  98.96426 107.65785
## 44.00000  92.56422 102.67746
## 44.14286  97.95759 109.20533
## 44.28571 113.27541 125.31834
## 44.42857 115.91836 128.54125
## 44.57143 112.88401 126.04881
## 44.71429 112.13283 126.68321
## 44.85714 105.98421 121.76626
## 45.00000 112.28118 129.34664
## 45.14286 117.57996 135.74258
## 45.28571 122.38701 141.36915
## 45.42857 123.50390 143.11531
## 45.57143 124.41274 144.62174
## 
## 
## $三重県
## $三重県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 4.536863 4.536863 4.536863 4.536863 4.536863 4.536863 4.536863 4.536863
##  [9] 4.536863 4.536863 4.536863 4.536863 4.536863 4.536863
## 
## $三重県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                  80%       95%
## 43.71429  0.81950238 -1.148350
## 43.85714  0.61296036 -1.464228
## 44.00000  0.41675937 -1.764292
## 44.14286  0.22948608 -2.050702
## 44.28571  0.05002247 -2.325168
## 44.42857 -0.12253395 -2.589070
## 44.57143 -0.28892418 -2.843542
## 44.71429 -0.44976551 -3.089527
## 44.85714 -0.60557863 -3.327823
## 45.00000 -0.75680755 -3.559108
## 45.14286 -0.90383456 -3.783966
## 45.28571 -1.04699158 -4.002906
## 45.42857 -1.18656901 -4.216371
## 45.57143 -1.32282265 -4.424753
## 
## $三重県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%      95%
## 43.71429  8.254224 10.22208
## 43.85714  8.460766 10.53796
## 44.00000  8.656967 10.83802
## 44.14286  8.844241 11.12443
## 44.28571  9.023704 11.39889
## 44.42857  9.196261 11.66280
## 44.57143  9.362651 11.91727
## 44.71429  9.523492 12.16325
## 44.85714  9.679305 12.40155
## 45.00000  9.830534 12.63283
## 45.14286  9.977561 12.85769
## 45.28571 10.120718 13.07663
## 45.42857 10.260296 13.29010
## 45.57143 10.396549 13.49848
## 
## 
## $滋賀県
## $滋賀県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 3.278552 4.765572 4.572528 3.502945 5.126847 4.755211 3.906440 4.933947
##  [9] 4.858301 4.093741 4.791677 4.862343 4.239099 4.686955
## 
## $滋賀県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                   80%       95%
## 43.71429 -0.396181895 -2.341469
## 43.85714  0.985492894 -1.015560
## 44.00000  0.273036653 -2.002976
## 44.14286 -0.844651212 -3.146130
## 44.28571  0.642748712 -1.730990
## 44.42857  0.136865700 -2.307939
## 44.57143 -0.768573617 -3.243376
## 44.71429  0.191354639 -2.319222
## 44.85714  0.005943704 -2.562739
## 45.00000 -0.810738674 -3.407013
## 45.14286 -0.171932590 -2.799509
## 45.28571 -0.192828377 -2.868874
## 45.42857 -0.871849419 -3.577422
## 45.57143 -0.479040822 -3.213754
## 
## $滋賀県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%       95%
## 43.71429 6.953286  8.898573
## 43.85714 8.545651 10.546704
## 44.00000 8.872018 11.148031
## 44.14286 7.850541 10.152020
## 44.28571 9.610946 11.984685
## 44.42857 9.373557 11.818361
## 44.57143 8.581453 11.056255
## 44.71429 9.676540 12.187117
## 44.85714 9.710657 12.279340
## 45.00000 8.998220 11.594495
## 45.14286 9.755286 12.382863
## 45.28571 9.917513 12.593559
## 45.42857 9.350048 12.055620
## 45.57143 9.852950 12.587663
## 
## 
## $京都府
## $京都府$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 16.59327 16.40927 16.00441 15.83041 18.25548 18.39397 18.13824 16.51281
##  [9] 17.18786 16.27214 16.57749 18.28548 17.69699 17.91474
## 
## $京都府$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%      95%
## 43.71429 10.266912 6.917938
## 43.85714  9.465923 5.790339
## 44.00000  8.821109 5.018497
## 44.14286  8.483719 4.594614
## 44.28571 10.766599 6.802226
## 44.42857 10.770475 6.734839
## 44.57143 10.383864 6.278947
## 44.71429  8.397689 4.101803
## 44.85714  8.862916 4.455955
## 45.00000  7.775458 3.277585
## 45.14286  7.921454 3.339227
## 45.28571  9.475534 4.811829
## 45.42857  8.736503 3.993108
## 45.57143  8.806420 3.984766
## 
## $京都府$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 22.91964 26.26861
## 43.85714 23.35261 27.02820
## 44.00000 23.18772 26.99033
## 44.14286 23.17711 27.06621
## 44.28571 25.74435 29.70873
## 44.42857 26.01747 30.05310
## 44.57143 25.89261 29.99753
## 44.71429 24.62793 28.92382
## 44.85714 25.51281 29.91977
## 45.00000 24.76883 29.26670
## 45.14286 25.23352 29.81575
## 45.28571 27.09543 31.75914
## 45.42857 26.65747 31.40087
## 45.57143 27.02307 31.84472
## 
## 
## $大阪府
## $大阪府$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 119.6583 180.1576 144.6473 163.6365 185.7577 196.5871 169.1344 146.8461
##  [9] 196.4857 161.1155 180.5096 201.4744 212.3558 186.9993
## 
## $大阪府$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%       95%
## 43.71429  96.88716  84.83286
## 43.85714 155.36655 142.24297
## 44.00000 117.98890 103.87682
## 44.14286 135.23331 120.19758
## 44.28571 155.71085 139.80502
## 44.42857 164.98198 148.25123
## 44.57143 136.04430 118.52744
## 44.71429 108.55996  88.29248
## 44.85714 155.76392 134.20708
## 45.00000 118.09572  95.32241
## 45.14286 135.30855 111.38054
## 45.28571 154.19257 129.16307
## 45.42857 163.08093 136.99641
## 45.57143 135.80903 108.71053
## 
## $大阪府$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 142.4294 154.4837
## 43.85714 204.9486 218.0722
## 44.00000 171.3056 185.4177
## 44.14286 192.0397 207.0754
## 44.28571 215.8045 231.7104
## 44.42857 228.1923 244.9230
## 44.57143 202.2246 219.7414
## 44.71429 185.1323 205.3998
## 44.85714 237.2076 258.7644
## 45.00000 204.1353 226.9086
## 45.14286 225.7107 249.6387
## 45.28571 248.7563 273.7858
## 45.42857 261.6306 287.7151
## 45.57143 238.1896 265.2881
## 
## 
## $兵庫県
## $兵庫県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 32.16328 49.47823 41.75067 45.18576 47.57117 38.75690 40.73651 41.21972
##  [9] 46.13496 43.28301 44.74030 45.38817 42.96648 43.51302
## 
## $兵庫県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 23.28045 18.57816
## 43.85714 40.13195 35.18433
## 44.00000 31.96286 26.78151
## 44.14286 34.97550 29.57051
## 44.28571 36.95526 31.33553
## 44.42857 27.75027 21.92371
## 44.57143 29.35257 23.32627
## 44.71429 28.02569 21.04120
## 44.85714 32.30725 24.98731
## 45.00000 28.84942 21.20874
## 45.14286 29.72525 21.77677
## 45.28571 29.81335 21.56855
## 45.42857 26.85133 18.32049
## 45.57143 26.87508 18.06748
## 
## $兵庫県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 41.04611 45.74840
## 43.85714 58.82451 63.77213
## 44.00000 51.53848 56.71983
## 44.14286 55.39602 60.80101
## 44.28571 58.18709 63.80682
## 44.42857 49.76353 55.59009
## 44.57143 52.12045 58.14675
## 44.71429 54.41375 61.39825
## 44.85714 59.96267 67.28261
## 45.00000 57.71660 65.35729
## 45.14286 59.75535 67.70384
## 45.28571 60.96298 69.20779
## 45.42857 59.08163 67.61247
## 45.57143 60.15096 68.95855
## 
## 
## $奈良県
## $奈良県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 11.24563 10.62529 10.64242 10.44411 10.42979 10.36440 10.35325 10.33105
##  [9] 10.32523 10.31749 10.31484 10.31207 10.31094 10.30994
## 
## $奈良県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 7.550079 5.593773
## 43.85714 6.808361 4.787802
## 44.00000 6.390999 4.140430
## 44.14286 6.057548 3.735445
## 44.28571 5.855503 3.434023
## 44.42857 5.668164 3.182125
## 44.57143 5.527665 2.973156
## 44.71429 5.394868 2.781813
## 44.85714 5.279241 2.608058
## 45.00000 5.168459 2.442729
## 45.14286 5.064720 2.285478
## 45.28571 4.964131 2.133101
## 45.42857 4.867040 1.985212
## 45.57143 4.772168 1.840649
## 
## $奈良県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 14.94118 16.89749
## 43.85714 14.44222 16.46278
## 44.00000 14.89385 17.14442
## 44.14286 14.83066 17.15276
## 44.28571 15.00407 17.42555
## 44.42857 15.06065 17.54669
## 44.57143 15.17883 17.73334
## 44.71429 15.26723 17.88028
## 44.85714 15.37121 18.04240
## 45.00000 15.46651 18.19224
## 45.14286 15.56495 18.34419
## 45.28571 15.66002 18.49105
## 45.42857 15.75485 18.63667
## 45.57143 15.84771 18.77923
## 
## 
## $和歌山県
## $和歌山県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 1.755512 2.190339 2.005465 1.834404 2.172533 1.133591 1.857118 1.849877
##  [9] 1.830483 1.830483 1.830483 1.830483 1.830483 1.830483
## 
## $和歌山県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                  80%        95%
## 43.71429 -0.11516860 -1.1054469
## 43.85714  0.18827135 -0.8715591
## 44.00000 -0.05987993 -1.1532076
## 44.14286 -0.29233716 -1.4181657
## 44.28571 -0.01388019 -1.1712974
## 44.42857 -1.11090856 -2.2990749
## 44.57143 -0.44400272 -1.6621422
## 44.71429 -0.45853930 -1.6805412
## 44.85714 -0.50962178 -1.7483984
## 45.00000 -0.54692065 -1.8054421
## 45.14286 -0.58364330 -1.8616046
## 45.28571 -0.61981565 -1.9169254
## 45.42857 -0.65546173 -1.9714414
## 45.57143 -0.69060384 -2.0251866
## 
## $和歌山県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 3.626192 4.616470
## 43.85714 4.192406 5.252237
## 44.00000 4.070810 5.164138
## 44.14286 3.961144 5.086973
## 44.28571 4.358946 5.516363
## 44.42857 3.378091 4.566257
## 44.57143 4.158238 5.376377
## 44.71429 4.158294 5.380296
## 44.85714 4.170588 5.409364
## 45.00000 4.207887 5.466408
## 45.14286 4.244609 5.522571
## 45.28571 4.280782 5.577891
## 45.42857 4.316428 5.632407
## 45.57143 4.351570 5.686153
## 
## 
## $鳥取県
## $鳥取県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 0.4870796 0.4272949 0.3827768 0.3496268 0.3249419 0.3065606 0.2928731
##  [8] 0.2826809 0.2750913 0.2694398 0.2652314 0.2620977 0.2597643 0.2580267
## 
## $鳥取県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                 80%        95%
## 43.71429 -0.3229716 -0.7517867
## 43.85714 -0.4002086 -0.8382625
## 44.00000 -0.4549265 -0.8983798
## 44.14286 -0.4942015 -0.9408972
## 44.28571 -0.5226711 -0.9713704
## 44.42857 -0.5434667 -0.9934439
## 44.57143 -0.5587500 -1.0095720
## 44.71429 -0.5700394 -1.0214422
## 44.85714 -0.5784157 -1.0302350
## 45.00000 -0.5846564 -1.0367877
## 45.14286 -0.5893255 -1.0417006
## 45.28571 -0.5928345 -1.0454083
## 45.42857 -0.5954854 -1.0482272
## 45.57143 -0.5975004 -1.0503891
## 
## $鳥取県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 1.297131 1.725946
## 43.85714 1.254798 1.692852
## 44.00000 1.220480 1.663933
## 44.14286 1.193455 1.640151
## 44.28571 1.172555 1.621254
## 44.42857 1.156588 1.606565
## 44.57143 1.144496 1.595318
## 44.71429 1.135401 1.586804
## 44.85714 1.128598 1.580418
## 45.00000 1.123536 1.575667
## 45.14286 1.119788 1.572163
## 45.28571 1.117030 1.569604
## 45.42857 1.115014 1.567756
## 45.57143 1.113554 1.566442
## 
## 
## $島根県
## $島根県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 0.4715719 0.4715719 0.4715719 0.4715719 0.4715719 0.4715719 0.4715719
##  [8] 0.4715719 0.4715719 0.4715719 0.4715719 0.4715719 0.4715719 0.4715719
## 
## $島根県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%       95%
## 43.71429 -6.377894 -10.00378
## 43.85714 -6.377894 -10.00378
## 44.00000 -6.377894 -10.00378
## 44.14286 -6.377894 -10.00378
## 44.28571 -6.377894 -10.00378
## 44.42857 -6.377894 -10.00378
## 44.57143 -6.377894 -10.00378
## 44.71429 -6.377894 -10.00378
## 44.85714 -6.377894 -10.00378
## 45.00000 -6.377894 -10.00378
## 45.14286 -6.377894 -10.00378
## 45.28571 -6.377894 -10.00378
## 45.42857 -6.377894 -10.00378
## 45.57143 -6.377894 -10.00378
## 
## $島根県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 7.321038 10.94693
## 43.85714 7.321038 10.94693
## 44.00000 7.321038 10.94693
## 44.14286 7.321038 10.94693
## 44.28571 7.321038 10.94693
## 44.42857 7.321038 10.94693
## 44.57143 7.321038 10.94693
## 44.71429 7.321038 10.94693
## 44.85714 7.321038 10.94693
## 45.00000 7.321038 10.94693
## 45.14286 7.321038 10.94693
## 45.28571 7.321038 10.94693
## 45.42857 7.321038 10.94693
## 45.57143 7.321038 10.94693
## 
## 
## $岡山県
## $岡山県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 6.447424 6.719304 5.103332 1.567803 5.910464 5.698645 7.163053 8.202241
##  [9] 5.954984 6.912924 7.569492 6.619957 6.538670 6.900187
## 
## $岡山県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%        95%
## 43.71429  3.905835  2.5603983
## 43.85714  3.903336  2.4126523
## 44.00000  2.178929  0.6308436
## 44.14286 -1.428844 -3.0151732
## 44.28571  2.852152  1.2331788
## 44.42857  2.582541  0.9329744
## 44.57143  3.991028  2.3118593
## 44.71429  4.908615  3.1650742
## 44.85714  2.581011  0.7949372
## 45.00000  3.470590  1.6483286
## 45.14286  4.063151  2.2070056
## 45.28571  3.051687  1.1627593
## 45.42857  2.909820  0.9888222
## 45.57143  3.211841  1.2593492
## 
## $岡山県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%      95%
## 43.71429  8.989014 10.33445
## 43.85714  9.535272 11.02596
## 44.00000  8.027734  9.57582
## 44.14286  4.564451  6.15078
## 44.28571  8.968777 10.58775
## 44.42857  8.814750 10.46432
## 44.57143 10.335079 12.01425
## 44.71429 11.495868 13.23941
## 44.85714  9.328957 11.11503
## 45.00000 10.355259 12.17752
## 45.14286 11.075834 12.93198
## 45.28571 10.188227 12.07715
## 45.42857 10.167521 12.08852
## 45.57143 10.588533 12.54102
## 
## 
## $広島県
## $広島県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 3.003058 3.001991 3.000593 3.001454 3.002015 3.001440 3.001263 3.001606
##  [9] 3.001627 3.001441 3.001469 3.001562 3.001527 3.001485
## 
## $広島県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                 80%       95%
## 43.71429 -0.5416105 -2.418045
## 43.85714 -0.9298902 -3.011303
## 44.00000 -1.1235456 -3.306733
## 44.14286 -1.7299944 -4.234672
## 44.28571 -2.2742358 -5.067314
## 44.42857 -2.5610449 -5.505646
## 44.57143 -2.8683726 -5.975570
## 44.71429 -3.2509804 -6.560900
## 44.85714 -3.5739969 -7.054922
## 45.00000 -3.8464587 -7.471518
## 45.14286 -4.1327969 -7.909449
## 45.28571 -4.4208254 -8.350000
## 45.42857 -4.6831781 -8.751215
## 45.57143 -4.9336649 -9.134280
## 
## $広島県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%       95%
## 43.71429  6.547727  8.424161
## 43.85714  6.933873  9.015285
## 44.00000  7.124732  9.307919
## 44.14286  7.732903 10.237581
## 44.28571  8.278265 11.071343
## 44.42857  8.563924 11.508525
## 44.57143  8.870900 11.978097
## 44.71429  9.254192 12.564111
## 44.85714  9.577251 13.058176
## 45.00000  9.849341 13.474400
## 45.14286 10.135735 13.912387
## 45.28571 10.423949 14.353123
## 45.42857 10.686233 14.754270
## 45.57143 10.936635 15.137250
## 
## 
## $山口県
## $山口県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 1.351684 1.351684 1.351684 1.351684 1.351684 1.351684 1.351684 1.351684
##  [9] 1.351684 1.351684 1.351684 1.351684 1.351684 1.351684
## 
## $山口県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                 80%       95%
## 43.71429 -0.5384796 -1.539072
## 43.85714 -0.6141377 -1.654781
## 44.00000 -0.6869899 -1.766199
## 44.14286 -0.7573270 -1.873770
## 44.28571 -0.8253929 -1.977868
## 44.42857 -0.8913943 -2.078808
## 44.57143 -0.9555083 -2.176862
## 44.71429 -1.0178883 -2.272264
## 44.85714 -1.0786676 -2.365218
## 45.00000 -1.1379636 -2.455903
## 45.14286 -1.1958799 -2.544479
## 45.28571 -1.2525084 -2.631084
## 45.42857 -1.3079315 -2.715847
## 45.57143 -1.3622229 -2.798878
## 
## $山口県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 3.241847 4.242439
## 43.85714 3.317505 4.358148
## 44.00000 3.390357 4.469566
## 44.14286 3.460695 4.577137
## 44.28571 3.528760 4.681235
## 44.42857 3.594762 4.782176
## 44.57143 3.658876 4.880230
## 44.71429 3.721256 4.975631
## 44.85714 3.782035 5.068585
## 45.00000 3.841331 5.159271
## 45.14286 3.899247 5.247846
## 45.28571 3.955876 5.334452
## 45.42857 4.011299 5.419214
## 45.57143 4.065590 5.502246
## 
## 
## $徳島県
## $徳島県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 0.46530433 0.63554460 0.48422408 0.49423940 1.04085383 0.03604171
##  [7] 0.41407320 0.47725032 0.42573020 0.48627386 0.48350080 0.06279812
## [13] 0.60671534 0.51501886
## 
## $徳島県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                 80%       95%
## 43.71429 -1.2743862 -2.195323
## 43.85714 -1.1873697 -2.152362
## 44.00000 -1.3576621 -2.332698
## 44.14286 -1.3664252 -2.351402
## 44.28571 -0.8384016 -1.833219
## 44.42857 -1.8616224 -2.866185
## 44.57143 -1.5018227 -2.516037
## 44.71429 -1.5371340 -2.603485
## 44.85714 -1.6234240 -2.708181
## 45.00000 -1.5874106 -2.685153
## 45.14286 -1.6144272 -2.725003
## 45.28571 -2.0590963 -3.182359
## 45.42857 -1.5388779 -2.674686
## 45.57143 -1.6540143 -2.802231
## 
## $徳島県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 2.204995 3.125931
## 43.85714 2.458459 3.423451
## 44.00000 2.326110 3.301146
## 44.14286 2.354904 3.339880
## 44.28571 2.920109 3.914927
## 44.42857 1.933706 2.938269
## 44.57143 2.329969 3.344183
## 44.71429 2.491635 3.557985
## 44.85714 2.474884 3.559641
## 45.00000 2.559958 3.657701
## 45.14286 2.581429 3.692005
## 45.28571 2.184693 3.307956
## 45.42857 2.752309 3.888117
## 45.57143 2.684052 3.832269
## 
## 
## $香川県
## $香川県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 0.7817802 0.4473202 0.4473202 0.4473202 0.4473202 0.4473202 0.4473202
##  [8] 0.4473202 0.4473202 0.4473202 0.4473202 0.4473202 0.4473202 0.4473202
## 
## $香川県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                 80%       95%
## 43.71429 -0.5823627 -1.304496
## 43.85714 -0.9781417 -1.732736
## 44.00000 -0.9820268 -1.738677
## 44.14286 -0.9859014 -1.744603
## 44.28571 -0.9897655 -1.750513
## 44.42857 -0.9936192 -1.756406
## 44.57143 -0.9974627 -1.762285
## 44.71429 -1.0012960 -1.768147
## 44.85714 -1.0051191 -1.773994
## 45.00000 -1.0089322 -1.779826
## 45.14286 -1.0127354 -1.785642
## 45.28571 -1.0165287 -1.791443
## 45.42857 -1.0203121 -1.797230
## 45.57143 -1.0240859 -1.803001
## 
## $香川県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 2.145923 2.868057
## 43.85714 1.872782 2.627376
## 44.00000 1.876667 2.633318
## 44.14286 1.880542 2.639243
## 44.28571 1.884406 2.645153
## 44.42857 1.888260 2.651047
## 44.57143 1.892103 2.656925
## 44.71429 1.895936 2.662787
## 44.85714 1.899759 2.668634
## 45.00000 1.903573 2.674466
## 45.14286 1.907376 2.680282
## 45.28571 1.911169 2.686084
## 45.42857 1.914952 2.691870
## 45.57143 1.918726 2.697642
## 
## 
## $愛媛県
## $愛媛県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 0.1386852 0.3567018 0.2763234 0.3934306 0.4118857 0.3917889 0.3085311
##  [8] 0.4018489 0.3544864 0.4120685 0.3635408 0.4101795 0.3675704 0.4072468
## 
## $愛媛県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%       95%
## 43.71429 -1.497059 -2.362969
## 43.85714 -1.325994 -2.216760
## 44.00000 -1.433696 -2.338925
## 44.14286 -1.317232 -2.222802
## 44.28571 -1.303407 -2.211428
## 44.42857 -1.325746 -2.234953
## 44.57143 -1.411434 -2.321929
## 44.71429 -1.321156 -2.233259
## 44.85714 -1.372737 -2.287073
## 45.00000 -1.315533 -2.230071
## 45.14286 -1.365104 -2.280193
## 45.28571 -1.319085 -2.234502
## 45.42857 -1.362310 -2.278053
## 45.57143 -1.323133 -2.239141
## 
## $愛媛県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 1.774429 2.640339
## 43.85714 2.039398 2.930163
## 44.00000 1.986342 2.891572
## 44.14286 2.104093 3.009663
## 44.28571 2.127178 3.035200
## 44.42857 2.109323 3.018531
## 44.57143 2.028496 2.938991
## 44.71429 2.124853 3.036957
## 44.85714 2.081709 2.996046
## 45.00000 2.139670 3.054208
## 45.14286 2.092185 3.007274
## 45.28571 2.139444 3.054861
## 45.42857 2.097450 3.013194
## 45.57143 2.137627 3.053635
## 
## 
## $高知県
## $高知県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 0.1696041 0.1915533 0.2118185 0.2305289 0.2478038 0.2637533 0.2784791
##  [8] 0.2920751 0.3046280 0.3162177 0.3269183 0.3367979 0.3459195 0.3543413
## 
## $高知県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%       95%
## 43.71429 -1.544845 -2.452419
## 43.85714 -1.544752 -2.463897
## 44.00000 -1.542904 -2.471798
## 44.14286 -1.539742 -2.476866
## 44.28571 -1.535613 -2.479698
## 44.42857 -1.530795 -2.480771
## 44.57143 -1.525503 -2.480473
## 44.71429 -1.519910 -2.479117
## 44.85714 -1.514152 -2.476956
## 45.00000 -1.508334 -2.474193
## 45.14286 -1.502539 -2.470995
## 45.28571 -1.496831 -2.467496
## 45.42857 -1.491258 -2.463801
## 45.57143 -1.485856 -2.459997
## 
## $高知県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 1.884053 2.791627
## 43.85714 1.927859 2.847004
## 44.00000 1.966541 2.895435
## 44.14286 2.000799 2.937924
## 44.28571 2.031221 2.975305
## 44.42857 2.058301 3.008278
## 44.57143 2.082461 3.037431
## 44.71429 2.104060 3.063267
## 44.85714 2.123408 3.086211
## 45.00000 2.140769 3.106629
## 45.14286 2.156376 3.124832
## 45.28571 2.170427 3.141091
## 45.42857 2.183097 3.155640
## 45.57143 2.194538 3.168680
## 
## 
## $福岡県
## $福岡県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1]  3.917637  4.214842  7.553970  7.966508 10.781391  7.898537  5.308413
##  [8]  4.598027  4.820842  7.054077  7.955220  9.425564  7.563917  5.919672
## 
## $福岡県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                 80%       95%
## 43.71429  -9.683672 -16.88377
## 43.85714 -11.761671 -20.21913
## 44.00000  -9.717703 -18.86078
## 44.14286 -10.014030 -19.53235
## 44.28571  -7.812662 -17.65576
## 44.42857 -11.584252 -21.89782
## 44.57143 -15.516594 -26.54069
## 44.71429 -18.841213 -31.24920
## 44.85714 -20.469317 -33.85712
## 45.00000 -19.472992 -33.51557
## 45.14286 -19.377591 -33.84671
## 45.28571 -18.603288 -33.44087
## 45.42857 -21.329588 -36.62489
## 45.57143 -24.150798 -40.06914
## 
## $福岡県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 17.51895 24.71904
## 43.85714 20.19135 28.64881
## 44.00000 24.82564 33.96872
## 44.14286 25.94704 35.46537
## 44.28571 29.37545 39.21854
## 44.42857 27.38133 37.69489
## 44.57143 26.13342 37.15751
## 44.71429 28.03727 40.44525
## 44.85714 30.11100 43.49880
## 45.00000 33.58115 47.62373
## 45.14286 35.28803 49.75715
## 45.28571 37.45442 52.29199
## 45.42857 36.45742 51.75272
## 45.57143 35.99014 51.90849
## 
## 
## $佐賀県
## $佐賀県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 0.9989247 0.9989247 0.9989247 0.9989247 0.9989247 0.9989247 0.9989247
##  [8] 0.9989247 0.9989247 0.9989247 0.9989247 0.9989247 0.9989247 0.9989247
## 
## $佐賀県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                 80%       95%
## 43.71429 -0.9700388 -2.012345
## 43.85714 -1.0499688 -2.134588
## 44.00000 -1.1268956 -2.252237
## 44.14286 -1.2011343 -2.365775
## 44.28571 -1.2729483 -2.475605
## 44.42857 -1.3425608 -2.582068
## 44.57143 -1.4101626 -2.685456
## 44.71429 -1.4759186 -2.786021
## 44.85714 -1.5399720 -2.883983
## 45.00000 -1.6024488 -2.979533
## 45.14286 -1.6634598 -3.072841
## 45.28571 -1.7231037 -3.164059
## 45.42857 -1.7814685 -3.253320
## 45.57143 -1.8386330 -3.340745
## 
## $佐賀県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 2.967888 4.010194
## 43.85714 3.047818 4.132437
## 44.00000 3.124745 4.250086
## 44.14286 3.198984 4.363624
## 44.28571 3.270798 4.473455
## 44.42857 3.340410 4.579918
## 44.57143 3.408012 4.683306
## 44.71429 3.473768 4.783871
## 44.85714 3.537821 4.881832
## 45.00000 3.600298 4.977382
## 45.14286 3.661309 5.070690
## 45.28571 3.720953 5.161908
## 45.42857 3.779318 5.251169
## 45.57143 3.836482 5.338595
## 
## 
## $長崎県
## $長崎県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] -0.12190056 -0.05958505 -0.09155518  0.11877670  0.45575598 -0.03381408
##  [7] -0.08145652 -0.10339573 -0.09219838 -0.09796192  0.02967329  0.27017122
## [13] -0.12574266 -0.09615447
## 
## $長崎県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%       95%
## 43.71429 -2.275186 -3.415066
## 43.85714 -2.455713 -3.724147
## 44.00000 -2.981281 -4.511010
## 44.14286 -3.060858 -4.744055
## 44.28571 -3.049047 -4.904378
## 44.42857 -3.807385 -5.804993
## 44.57143 -4.119509 -6.257125
## 44.71429 -4.534960 -6.880888
## 44.85714 -4.812953 -7.311970
## 45.00000 -5.125250 -7.786536
## 45.14286 -5.269801 -8.075173
## 45.28571 -5.296273 -8.242970
## 45.42857 -5.942949 -9.022392
## 45.57143 -6.155703 -9.363434
## 
## $長崎県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 2.031385 3.171265
## 43.85714 2.336543 3.604977
## 44.00000 2.798171 4.327900
## 44.14286 3.298412 4.981609
## 44.28571 3.960559 5.815890
## 44.42857 3.739757 5.737365
## 44.57143 3.956596 6.094212
## 44.71429 4.328168 6.674097
## 44.85714 4.628556 7.127573
## 45.00000 4.929326 7.590612
## 45.14286 5.329147 8.134519
## 45.28571 5.836615 8.783313
## 45.42857 5.691464 8.770907
## 45.57143 5.963394 9.171125
## 
## 
## $熊本県
## $熊本県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 8.605663 6.966305 7.273412 7.273412 7.273412 7.273412 7.273412 7.273412
##  [9] 7.273412 7.273412 7.273412 7.273412 7.273412 7.273412
## 
## $熊本県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                  80%       95%
## 43.71429  4.40697811  2.184329
## 43.85714  1.36862236 -1.594612
## 44.00000  1.52296818 -1.521133
## 44.14286  1.18333360 -2.040559
## 44.28571  0.86166451 -2.532510
## 44.42857  0.55537979 -3.000932
## 44.57143  0.26246288 -3.448909
## 44.71429 -0.01869733 -3.878907
## 44.85714 -0.28941217 -4.292930
## 45.00000 -0.55076593 -4.692636
## 45.14286 -0.80366737 -5.079415
## 45.28571 -1.04888709 -5.454446
## 45.42857 -1.28708524 -5.818739
## 45.57143 -1.51883253 -6.173166
## 
## $熊本県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 12.80435 15.02700
## 43.85714 12.56399 15.52722
## 44.00000 13.02386 16.06796
## 44.14286 13.36349 16.58738
## 44.28571 13.68516 17.07933
## 44.42857 13.99144 17.54776
## 44.57143 14.28436 17.99573
## 44.71429 14.56552 18.42573
## 44.85714 14.83624 18.83975
## 45.00000 15.09759 19.23946
## 45.14286 15.35049 19.62624
## 45.28571 15.59571 20.00127
## 45.42857 15.83391 20.36556
## 45.57143 16.06566 20.71999
## 
## 
## $大分県
## $大分県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 0.5862552 0.5269432 0.5427081 0.4093367 0.4148522 0.4200738 0.4250171
##  [8] 0.4296969 0.4341273 0.4383216 0.4422923 0.4460515 0.4496102 0.4529793
## 
## $大分県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                 80%       95%
## 43.71429 -0.7966074 -1.528651
## 43.85714 -1.0676555 -1.911785
## 44.00000 -1.2229344 -2.157609
## 44.14286 -1.4209384 -2.389827
## 44.28571 -1.4249950 -2.398951
## 44.42857 -1.4283104 -2.406786
## 44.57143 -1.4309849 -2.413493
## 44.71429 -1.4331061 -2.419214
## 44.85714 -1.4347500 -2.424074
## 45.00000 -1.4359831 -2.428180
## 45.14286 -1.4368634 -2.431628
## 45.28571 -1.4374413 -2.434502
## 45.42857 -1.4377611 -2.436875
## 45.57143 -1.4378615 -2.438812
## 
## $大分県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 1.969118 2.701161
## 43.85714 2.121542 2.965671
## 44.00000 2.308351 3.243025
## 44.14286 2.239612 3.208501
## 44.28571 2.254699 3.228656
## 44.42857 2.268458 3.246933
## 44.57143 2.281019 3.263527
## 44.71429 2.292500 3.278608
## 44.85714 2.303005 3.292329
## 45.00000 2.312626 3.304823
## 45.14286 2.321448 3.316213
## 45.28571 2.329544 3.326605
## 45.42857 2.336982 3.336096
## 45.57143 2.343820 3.344771
## 
## 
## $宮崎県
## $宮崎県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 0.3309710 0.4680038 0.4680038 0.4680038 0.4680038 0.4680038 0.4680038
##  [8] 0.4680038 0.4680038 0.4680038 0.4680038 0.4680038 0.4680038 0.4680038
## 
## $宮崎県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%       95%
## 43.71429 -2.386224 -3.824620
## 43.85714 -2.364793 -3.864385
## 44.00000 -2.692507 -4.365580
## 44.14286 -2.989295 -4.819479
## 44.28571 -3.262546 -5.237380
## 44.42857 -3.517105 -5.626694
## 44.57143 -3.756352 -5.992591
## 44.71429 -3.982757 -6.338847
## 44.85714 -4.198190 -6.668323
## 45.00000 -4.404106 -6.983245
## 45.14286 -4.601665 -7.285385
## 45.28571 -4.791809 -7.576185
## 45.42857 -4.975315 -7.856834
## 45.57143 -5.152833 -8.128324
## 
## $宮崎県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 3.048166 4.486561
## 43.85714 3.300801 4.800393
## 44.00000 3.628514 5.301588
## 44.14286 3.925303 5.755486
## 44.28571 4.198554 6.173388
## 44.42857 4.453113 6.562702
## 44.57143 4.692360 6.928599
## 44.71429 4.918765 7.274855
## 44.85714 5.134197 7.604331
## 45.00000 5.340113 7.919252
## 45.14286 5.537672 8.221393
## 45.28571 5.727816 8.512193
## 45.42857 5.911322 8.792841
## 45.57143 6.088841 9.064332
## 
## 
## $鹿児島県
## $鹿児島県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 8.193686 6.471825 5.415349 4.767130 4.369404 4.125373 3.975644 3.883775
##  [9] 3.827408 3.792822 3.771602 3.758582 3.750593 3.745691
## 
## $鹿児島県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                 80%        95%
## 43.71429  4.2141158  2.1074587
## 43.85714  1.7365079 -0.7702175
## 44.00000  0.3843679 -2.2788724
## 44.14286 -0.3974858 -3.1314682
## 44.28571 -0.8632092 -3.6331875
## 44.42857 -1.1462998 -3.9369551
## 44.57143 -1.3213836 -4.1254607
## 44.71429 -1.4316824 -4.2455157
## 44.85714 -1.5027798 -4.3244105
## 45.00000 -1.5500262 -4.3783593
## 45.14286 -1.5827104 -4.4171121
## 45.28571 -1.6064831 -4.4465768
## 45.42857 -1.6247928 -4.4703501
## 45.57143 -1.6397483 -4.4906279
## 
## $鹿児島県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%      95%
## 43.71429 12.173256 14.27991
## 43.85714 11.207142 13.71387
## 44.00000 10.446329 13.10957
## 44.14286  9.931746 12.66573
## 44.28571  9.602018 12.37200
## 44.42857  9.397047 12.18770
## 44.57143  9.272672 12.07675
## 44.71429  9.199233 12.01307
## 44.85714  9.157595 11.97923
## 45.00000  9.135671 11.96400
## 45.14286  9.125914 11.96032
## 45.28571  9.123646 11.96374
## 45.42857  9.125979 11.97154
## 45.57143  9.131131 11.98201
## 
## 
## $沖縄県
## $沖縄県$mean
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##  [1] 24.74910 26.35553 25.46081 27.82212 26.81133 29.08371 28.73082 28.22391
##  [9] 28.22391 28.22391 28.22391 28.22391 28.22391 28.22391
## 
## $沖縄県$lower
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##                80%       95%
## 43.71429 13.023263  6.815982
## 43.85714 13.527850  6.737287
## 44.00000 11.618720  4.291158
## 44.14286 13.035037  5.207229
## 44.28571 11.136129  2.838177
## 44.42857 12.568073  3.825224
## 44.57143 11.415493  2.249314
## 44.71429  9.174636 -0.909439
## 44.85714  8.123015 -2.517755
## 45.00000  7.123741 -4.046012
## 45.14286  6.169698 -5.505096
## 45.28571  5.255248 -6.903626
## 45.42857  4.375837 -8.248569
## 45.57143  3.527722 -9.545649
## 
## $沖縄県$upper
## Time Series:
## Start = c(43, 6) 
## End = c(45, 5) 
## Frequency = 7 
##               80%      95%
## 43.71429 36.47493 42.68221
## 43.85714 39.18321 45.97377
## 44.00000 39.30291 46.63047
## 44.14286 42.60920 50.43701
## 44.28571 42.48653 50.78449
## 44.42857 45.59934 54.34219
## 44.57143 46.04614 55.21232
## 44.71429 47.27318 57.35726
## 44.85714 48.32480 58.96557
## 45.00000 49.32407 60.49383
## 45.14286 50.27812 61.95291
## 45.28571 51.19257 63.35144
## 45.42857 52.07198 64.69638
## 45.57143 52.92009 65.99347